Question: The Browsemaps: Implementing Collaborative Filtering within LinkedIn's Network.

This document introduces MapReduce, a groundbreaking programming model and implementation designed for the efficient processing and generation of massive datasets, applicable to a wide spectrum of practical tasks. The system's uniqueness lies in its ability to allow users to define computations through the specification of a map function and a reduce function. The underlying runtime system then takes the reins, automating the parallelization of the computation across extensive clusters of machines, managing machine failures, and optimizing inter-machine communication for enhanced network and disk utilization efficiency. Notably, the simplicity and ease of use of this system have been widely appreciated by programmers. Over the past four years, Google has internally developed over ten thousand distinct MapReduce programs, with an average of one hundred thousand MapReduce jobs being executed daily on their clusters. These jobs collectively process more than twenty petabytes of data daily, underscoring the system's remarkable scalability and efficiency in handling large-scale data processing tasks.

Personalized recommendations have become a common feature of many modern online services. In particular on ecommerce sites, one value of such recommendations is that they help consumers find items of interest in large product assortments more quickly. Many of today’s sites take advantage of modern recommendation technologies to create personalized item suggestions for consumers navigating the site. However, limited research exists on the use of personalization and recommendation technology when consumers rely on the site’s catalog search functionality to discover relevant items. In this work we explore the value of personalizing search results on e-commerce sites using recommendation technology. We design and evaluate different personalization strategies using log data of an online retail site. Our results show that considering several item relevance signals within the recommendation process in parallel leads to the best ranking of the search results. Specifically, the factors taken into account include the users’ general interests, their most recent browsing behavior, as well as the consideration of current sales trends.

Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.

This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.

Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.

How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.

The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.

This article describes a novel and fast recommender system for websites based on product taxonomy and user click patterns. The proposed system consists of the following four steps. First, a product-preference matrix for each customer is estimated through a linear combination of the click, basket placement, and purchase statuses. Second, the preference matrix of the genre and that of the specific type are constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product to the totals. Third, cluster analysis is performed using the genre preference matrix, and a neighborhood formation process is conducted using the specific-type preference matrix. Finally, data are generated for prediction, in which a customer’s preference for specific types is greater than a given threshold value. Using these data sets, computational burden and processing time are greatly reduced. The effectiveness of the proposed approach was assessed by applying the F1 metric to an experimental e-commerce website. The proposed method outperformed conventional methods.

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

Despite the wealth of information contained in the Web of Linked Data, the current limitations and entry barriers of the Semantic Web technologies hinder the users from taking advantage of these information resources. Linked Data visualization can alleviate this problem. In this paper, we adopt a proper Linked Data visualization model, design Linked Data visualization algorithms, and develop a lightweight, easy-to-use prototype tool, LOD Viewer, using the platform independent JavaScript language. LOD Viewer can visualize different sources of RDF data including SPARQL endpoints for Linked Open Data (LOD) sources, and display the data in different graphic illustrations. Our case studies have verified the effectiveness and realizability of the proposed method. The time complexity analysis and experimental test show that the run-time of the proposed algorithms approximately exhibits a linear growth rate as the visualized RDF triples size increases.

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.


Question: EvoloPy: An open-source framework for nature-inspired optimization in Python.

This paper delves into the intricate relationship between effective optimization algorithms and the problems they address, culminating in the formulation of several "no free lunch" (NFL) theorems. These theorems elucidate that any optimization algorithm's superior performance on one set of problems is invariably offset by inferior performance on another set. This finding leads to a geometric understanding of how an algorithm is well-suited to a specific optimization problem. The paper further discusses the implications of the NFL theorems on the information-theoretic aspects of optimization and benchmark measures of algorithmic performance. It also tackles the challenge of time-varying optimization problems and explores a priori head-to-head minimax distinctions between optimization algorithms. Despite the NFL theorems enforcing a kind of uniformity across all algorithms, these distinctions can still be made, offering insights into the comparative advantages of different optimization strategies.

**Question**: A Deep Learning Framework for Financial Prediction: Using Stacked Autoencoders and Long-Short Term Memory

**Documents**: Deep Learning for Stock Prediction
Deep learning has been successfully applied to many domains including computer vision speech recognition and natural language processing This paper presents the first attempt to apply deep learning to stock prediction by using a stacked autoencoder to extract features from stock market data and then feeding the extracted features to a long short term memory LSTM network to predict stock prices We evaluate the performance of our proposed model on the S P stock index and compare it with several state of the art stock prediction models The experimental results show that our proposed model outperforms the other models in terms of prediction accuracy and robustness

**Similar Documents**: This paper introduces the pioneering application of deep learning techniques to the realm of stock prediction. Building on the successes of deep learning in fields such as computer vision, speech recognition, and natural language processing, we propose a novel framework that combines stacked autoencoders with a Long Short-Term Memory (LSTM) network. The stacked autoencoder is tasked with feature extraction from stock market data, while the LSTM network leverages these features for predicting stock prices. We assess the efficacy of our model using the S&P 500 stock index, comparing its performance against several leading stock prediction models. Our findings reveal that the proposed model significantly surpasses alternative models in terms of both prediction accuracy and robustness, marking a significant advancement in the application of deep learning to financial market forecasting.

In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and mixes building blocks up to a speci ed order It is independent of the ordering of the variables in the strings rep resenting the solutions Moreover prior in formation about the problem can be incor porated into the algorithm However prior information is not essential Preliminary ex periments show that the BOA outperforms the simple genetic algorithm even on decom posable functions with tight building blocks as a problem size grows

This paper introduces ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by pheromone they deposit on the edges of the TSP graph while building solutions. We study ACS by running experiments to understand its operation. The results show that ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs. Accepted for publication in the IEEE Transactions on Evolutionary Computation, Vol.1, No.1, 1997. In press.

NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.

Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (tomultiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research,mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

Sherpa is a free open-source hyperparameter optimization library for machine learning models. It is designed for problems with computationally expensive iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Additionally, the framework makes it easy to implement custom algorithms. Sherpa can be run on either a single machine or a cluster via a grid scheduler with minimal configuration. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning researchers by automating the tedious aspects of model tuning and providing an extensible framework for developing automated hyperparameter-tuning strategies. Its source code and documentation are available at https://github.com/LarsHH/sherpa and https://parameter-sherpa.readthedocs.io/, respectively. A demo can be found at https://youtu.be/L95sasMLgP4. 1 Existing Hyperparameter Optimization Libraries Hyperparameter optimization algorithms for machine learning models have previously been implemented in software packages such as Spearmint [15], HyperOpt [2], Auto-Weka 2.0 [9], and Google Vizier [5] among others. Spearmint is a Python library based on Bayesian optimization using a Gaussian process. Hyperparameter exploration values are specified using the markup language YAML and run on a grid via SGE and MongoDB. Overall, it combines Bayesian optimization with the ability for distributed training. HyperOpt is a hyperparameter optimization framework that uses MongoDB to allow parallel computation. The user manually starts workers which receive tasks from the HyperOpt instance. It offers the use of Random Search and Bayesian optimization based on a Tree of Parzen Estimators. Auto-WEKA 2.0 implements the SMAC [6] algorithm for automatic model selection and hyperparameter optimization within the WEKA machine learning framework. It provides a graphical user interface and supports parallel runs on a single machine. It is meant to be accessible for novice users and specifically targets the problem of choosing a model. Auto-WEKA is related to Auto-Sklearn [4] and Auto-Net [11] which specifically focus on tuning Scikit-Learn models and fully-connected 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Table 1: Comparison to Existing Libraries Spearmint Auto-WEKA HyperOpt Google Vizier Sherpa Early Stopping No No No Yes Yes Dashboard/GUI Yes Yes No Yes Yes Distributed Yes No Yes Yes Yes Open Source Yes Yes Yes No Yes # of Algorithms 2 1 2 3 5 neural networks in Lasagne, respectively. Auto-WEKA, Auto-Sklearn, and Auto-Net focus on an end-to-end automatic approach. This makes it easy for novice users, but restricts the user to the respective machine learning library and the models it implements. In contrast our work aims to give the user more flexibility over library, model and hyper-parameter optimization algorithm selection. Google Vizier is a service provided by Google for its cloud machine learning platform. It incorporates recent innovation in Bayesian optimization such as transfer learning and provides visualizations via a dashboard. Google Vizier provides many key features of a current hyperparameter optimization tool to Google Cloud users and Google engineers, but is not available in an open source version. A similar situation occurs with other cloud based platforms like Microsoft Azure Hyperparameter Tuning 1 and Amazon SageMaker’s Hyperparameter Optimization 2. 2 Need for a new library The field of machine learning has experienced massive growth over recent years. Access to open source machine learning libraries such as Scikit-Learn [14], Keras [3], Tensorflow [1], PyTorch [13], and Caffe [8] allowed research in machine learning to be widely reproduced by the community making it easy for practitioners to apply state of the art methods to real world problems. The field of hyperparameter optimization for machine learning has also seen many innovations recently such as Hyperband [10], Population Based Training [7], Neural Architecture Search [17], and innovation in Bayesian optimization such as [16]. While the basic implementation of some of these algorithms can be trivial, evaluating trials in a distributed fashion and keeping track of results becomes cumbersome which makes it difficult for users to apply these algorithms to real problems. In short, Sherpa aims to curate implementations of these algorithms while providing infrastructure to run these in a distributed way. The aim is for the platform to be scalable from usage on a laptop to a computation grid.

Clustering is a process that aims to group the similar records in one cluster and dissimilar records in different clusters. K-means is one of the most popular and well-known clustering technique for its simplicity and light weight. However, the main drawback of K-means clustering technique is that it requires a user (data miner) to estimate the number of clusters in advance. Another limitation of K-means is that it has a tendency to get stuck at local optima. In order to overcome these limitations many evolutionary algorithm based clustering techniques have been proposed since the 1990s and applied to various fields. In this paper, we present an up-to-date review of some major evolutionary algorithm based clustering techniques for the last twenty (20) years (1995-2015). A total of 63 ranked (i.e. based on citation reports and JCR/CORE rank) evolutionary algorithm based clustering approaches are reviewed. Maximum of the techniques do not require any user to define the number of clusters in advance. We present the limitations and advantages of some evolutionary algorithm based clustering techniques. We also present a thorough discussion and future research directions of evolutionary algorithm based clustering techniques.

In this paper, we present an empirical comparison of some Differential Evolution variants to solve global optimization problems. The aim is to identify which one of them is more suitable to solve an optimization problem, depending on the problem's features and also to identify the variant with the best performance, regardless of the features of the problem to be solved. Eight variants were implemented and tested on 13 benchmark problems taken from the specialized literature. These variants vary in the type of recombination operator used and also in the way in which the mutation is computed. A set of statistical tests were performed in order to obtain more confidence on the validity of the results and to reinforce our discussion. The main aim is that this study can help both researchers and practitioners interested in using differential evolution as a global optimizer, since we expect that our conclusions can provide some insights regarding the advantages or limitations of each of the variants studied.

The No Free Lunch (NFL) theorems for search and optimization are reviewed and their implications for the design of metaheurist ics are discussed. The theorems state that any two search or optimization algorith ms are equivalent when their performance is averaged across all possible problems and even over subsets of problems fulfilling certain constraints. The NFL results show that if there is no assumption regarding the relation between visited and unse en arch points, efficient search and optimization is impossible. There is no wel l performing universal metaheuristic, but the heuristics must be tailored to the pr oblem class at hand using prior knowledge. In practice, it is not likely that the pr econditions of the NFL theorems are fulfilled for a problem class and thus differenc es between algorithms exist. Therefore, tailored algorithms can exploit structu re nderlying the optimization problem. Given full knowledge about the problem class, it i in theory possible to construct an optimal algorithm.

Hybridization in context to Evolutionary Computation (EC) aims at combining the operators and methodologies from different EC paradigms to form a single algorithm that may enjoy a statistically superior performance on a wide variety of optimization problems. In this article we propose an efficient hybrid evolutionary algorithm that embeds the difference vector-based mutation scheme, the crossover and the selection strategy of Differential Evolution (DE) into another recently developed global optimization algorithm known as Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). CMA-ES is a stochastic method for real parameter (continuous domain) optimization of non-linear, non-convex functions. The algorithm includes adaptation of covariance matrix which is basically an alternative method of traditional Quasi-Newton method for optimization based on gradient method. The hybrid algorithm, referred by us as Differential Covariance Matrix Adaptation Evolutionary Algorithm (DCMA-EA), turns out to possess a better blending of the explorative and exploitative behaviors as compared to the original DE and original CMAES, through empirical simulations. Though CMA-ES has emerged itself as a very efficient global optimizer, its performance deteriorates when it comes to dealing with complicated fitness landscapes, especially landscapes associated with noisy, hybrid composition functions and many real world optimization problems. In order to improve the overall performance of CMA-ES, the mutation, crossover and selection operators of DE have been incorporated into CMA-ES to synthesize the hybrid algorithm DCMA-EA. We compare DCMA-EA with original DE and CMA-EA, two best known DE-variants: SaDE and JADE, and two state-of-the-art real optimizers: IPOP-CMA-ES (Restart Covariance Matrix Adaptation Evolution Strategy with increasing population size) and DMS-PSO (Dynamic Multi Swarm Particle Swarm Optimization) over a test-suite of 20 shifted, rotated, and compositional benchmark functions and also two engineering optimization problems. Our comparative study indicates that although the hybridization scheme does not impose any serious burden on DCMA-EA in terms of number of Function Evaluations (FEs), DCMA-EA still enjoys a statistically superior performance over most of the tested benchmarks and especially over the multi-modal, rotated, and compositional ones in comparison to the other algorithms considered here. 2011 Published by Elsevier Inc. y Elsevier Inc. osh), swagatam.das@isical.ac.in (S. Das), roy.subhrajit20@gmail.com (S. Roy), skminha.isl@gmail.com ganthan). 200 S. Ghosh et al. / Information Sciences 182 (2012) 199–219

Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers – one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.


Question: EvoloPy: An open-source framework for nature-inspired optimization in Python.

Introducing the Dolphin Herding Algorithm: A Nature-Inspired Optimization Framework

This research paper presents a new meta-heuristic optimization method, the Dolphin Herding Algorithm (DHA), which is inspired by the hunting behavior of dolphins. DHA emulates the herding strategy employed by dolphins to capture prey, showcasing the sophisticated social dynamics of these marine mammals. The algorithm is rigorously evaluated against 29 mathematical optimization problems and six structural design challenges, demonstrating its efficacy through comparative analysis with cutting-edge meta-heuristic algorithms and traditional optimization techniques. The superior performance of DHA in solving complex optimization tasks is evident from the optimization outcomes. To facilitate further research and application, the source code for the DHA algorithm is freely accessible at a designated online repository, enabling the scientific community to explore and extend its capabilities. © 2023 Academic Publishers. All rights reserved.

Geometric crossover is a representation-independent definition of crossover based on the distance of the search space interpreted as a metric space. It generalizes the traditional crossover for binary strings and other important recombination operators for the most used representations. Using a distance tailored to the problem at hand, the abstract definition of crossover can be used to design new problem specific crossovers that embed problem knowledge in the search. In recent work, we have introduced the important notion of product geometric crossover that enables the construction of new geometric crossovers combining preexisting geometric crossovers in a simple way. In this paper, we use it to design an evolutionary algorithm to solve the Sudoku puzzle. The different types of constraints make Sudoku an interesting study case for crossover design. We conducted extensive experimental testing and found that, on medium and hard problems, the new geometric crossovers perform significantly better than hill-climbers and mutations alone.

In 1994, Karl Sims' evolved virtual creatures showed the potential of evolutionary algorithms to produce natural, complex morphologies and behaviors [30]. One might assume that nearly 20 years of improvements in computational speed and evolutionary algorithms would produce far more impressive organisms, yet the creatures evolved in the field of artificial life today are not obviously more complex, natural, or intelligent. Fig. 2 demonstrates an example of similar complexity in robots evolved 17 years apart.

There is no other package around that satisfies all the following requirements: •Ability to handle large graphs efficiently •Embeddable into higher level environments (like R [6] or Python [7]) •Ability to be used for quick prototyping of new algorithms (impossible with “click & play” interfaces) •Platform-independent and open source igraph aims to satisfy all these requirements while possibly remaining easy to use in interactive mode as well.

This paper demonstrates the computational advantages of a multiobjective framework that can overcome the generic and domain-related challenges in optical system design and optimisation. Non-dominated sorting genetic algorithms-II (Deb, 2003) is employed in this study. The optical systems studied in this paper are Cooke triplets, Petzval lens systems and achromatic doublets. We report the results of four studies. In the first study, we optimise the optical systems using computationally efficient image quality objective functions. Our approach uses only two paraxial rays to estimate the objective functions and thus improves the computational efficiency. This timesaving measure can partially compensate for the typically enormous number of fitness function evaluations required in evolutionary algorithms. The reduction in reliability due to the computations from a single ray pair is compensated by the availability of multiple objective functions that help us to navigate to the optima. In the second study, hybridisation of evolutionary and gradient-based approaches and scaling techniques are employed to speed up convergence and enforce the constraints. The third study shows how recent developments in optical system design research can be better integrated in a multi-objective framework. The fourth study optimises an achromatic doublet with suitable constraints applied to the thicknesses and image distance.

In this paper, we present an empirical comparison of some Differential Evolution variants to solve global optimization problems. The aim is to identify which one of them is more suitable to solve an optimization problem, depending on the problem's features and also to identify the variant with the best performance, regardless of the features of the problem to be solved. Eight variants were implemented and tested on 13 benchmark problems taken from the specialized literature. These variants vary in the type of recombination operator used and also in the way in which the mutation is computed. A set of statistical tests were performed in order to obtain more confidence on the validity of the results and to reinforce our discussion. The main aim is that this study can help both researchers and practitioners interested in using differential evolution as a global optimizer, since we expect that our conclusions can provide some insights regarding the advantages or limitations of each of the variants studied.

This paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do. PyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification. PyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.

In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.

Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. However there are still many areas in which humans excel in comparison with the machines. One such area is chess. Even with great advances in the speed and computational power of modern machines, Grandmasters often beat the best chess programs in the world with relative ease1. This may be due to the fact that a game of chess cannot be won by pure calculation. There is more to the goodness of a chess position than some numerical value which apparently can be seen only by the human brain. Here an effort has been made to improve current chess engines by letting themselves evolve over a period of time. Firstly, the problem of learning is reduced into an optimization problem by defining Position Evaluation in terms of Positional Value Tables (PVTs). Next, the PVTs are optimized using Multi-Niche Crowding which successfully identifies the optima in a multimodal function, thereby arriving at distinctly different solutions which are close to the global optimum.

It is widely believed that in order to solve large scale global optimization problems an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a “blind” procedure, aimed at placing observations as evenly as possible in the search domain. Often this procedure reduces to uniform random sampling (like in Multistart algorithms, or in techniques based on clustering). In this paper we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local searches seems to have never been explored yet. Although an exact smoothing of the results of local searches is impossible to implement, in this paper we propose a computational approximation scheme which has proven to be very efficient and (maybe more important) extremely robust in solving large scale global optimization problems with huge numbers of local optima. ∗Email: b.addis@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze †Email: locatell@di.unito.it Dip. Informatica Università di Torino ‡Email: schoen@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze

This paper presents a method to employ particle swarms optim izers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each w hich is optimized cooperatively in its own swarm. The applic ation of this technique to neural network training is investigate d, with promising results.


Question: EvoloPy: An open-access nature-inspired optimization framework implemented in Python.

In "Genetic Programming: On the Programming of Computers by Means of Natural Selection," John R. Koza presents a pioneering approach to programming computers that mirrors the process of natural selection. This methodology, known as Genetic Programming (GP), automates the creation of computer programs by employing the principles of evolution, such as mutation, crossover, and selection. Inspired by the complexity and adaptability of natural systems, GP seeks to harness the power of evolutionary algorithms to generate software solutions that can evolve and adapt over time. The book, part of the Complex Adaptive Systems series edited by John H. Holland, Christopher Langton, and Stewart W. Wilson, provides an in-depth exploration of GP and its applications in various fields, including biology, control systems, and artificial intelligence. It is a foundational text for anyone interested in understanding how evolutionary principles can be applied to solve complex programming challenges.

**Question**: An Efficient PSO Algorithm for Solving Economic Load Dispatch Problem

**Documents**: An efficient PSO algorithm for solving economic load dispatch problem
Economic load dispatch is one of the most important optimization problems in power systems. In this paper, an efficient particle swarm optimization (PSO) algorithm is proposed to solve the economic load dispatch problem. The proposed algorithm is a modified version of the standard PSO algorithm, which incorporates the concept of inertia weight and constriction factor to improve the convergence speed and solution quality. The proposed algorithm is tested on the IEEE 30-bus test system with 6 generators, and the results are compared with those obtained by other optimization algorithms such as genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE). The results show that the proposed algorithm outperforms the other algorithms in terms of solution quality and computational efficiency.

**Similar Documents**: This paper introduces an enhanced particle swarm optimization (PSO) algorithm designed to address the economic load dispatch problem in power systems more efficiently. Economic load dispatch, a critical optimization task, requires the allocation of electrical load among generators in a manner that minimizes the overall cost while satisfying operational constraints. The proposed PSO variant, which incorporates the concepts of inertia weight and constriction factor, aims to enhance the convergence rate and the quality of the solution. Through rigorous testing on the IEEE 30-bus system, featuring 6 generators, the algorithm's performance is compared against established optimization techniques, including genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE). The comparative analysis reveals that the modified PSO algorithm delivers superior results in terms of both solution quality

Implementing E cient, Portable Computations for Machine Learning

In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.

If you are looking for Genetic Algorithms Data Structures Evolution Programs in pdf file you can find it here. This is the best place for you where you can find the genetic algorithms data structures evolution programs document. Sign up for free to get the download links. There are 3 sources of download links that you can download and save it in your desktop.

In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid PSOs combine the traditional velocity and position update rules with the ideas of breeding and subpopulations. Both hybrid models were tested and compared with the standard PSO and standard GA models. This is done to illustrate that PSOs with breeding strategies have the potential to achieve faster convergence and the potential to find a better solution. The objective of this paper is to describe how to make the hybrids benefit from genetic methods and to test their potential and competetiveness on function optimisation.

Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.

A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method. Our main contribution lies in dealing with high-dimensionality minimization problems, which are often difficult to solve by all known minimization methods with or without gradient. In this article we take a special interest in the variables discretization issue. We also develop and implement several complementary stopping criteria. The original Metropolis iterative random search, which takes place in a Euclidean space Rn, is replaced by another similar exploration, performed within a succession of Euclidean spaces Rp, with p <<n. This Enhanced Simulated Annealing (ESA) algorithm was validated first on classical highly multimodal functions of 2 to 100 variables. We obtained significant reductions in the number of function evaluations compared to six other global optimization algorithms, selected according to previously published computational results for the same set of test functions. In most cases, ESA was able to closely approximate known global optima. The reduced ESA computational cost helped us to refine further the obtained global results, through the use of some local search. We have used this new minimizing procedure to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.

There has been significant recent interest towards achievin g highly efficient deep neural network architectures. A promising paradigm for ach ieving this is the concept ofevolutionary deep intelligence , which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural n etworks over successive generations. An important aspect of evolutionary deep inte lligence is the genetic encoding scheme used to mimic heredity, which can have a sign ificant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genet ic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of ima ge classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art perf ormance while having network architectures that are not only significantly more e ffici nt (with a∼125fold decrease in synapses for MNIST) compared to the origina l a cestor network, but also tailored for GPU-accelerated machine learning app lications.

A new paradigm of the evolving computational intelligence systems (ECIS) is introduced in a generic framework of the knowledge and data integration (KDI). This generalization of the recent advances in the development of evolving fuzzy and neuro-fuzzy models and the more analytical angle of consideration through the prism of knowledge evolution as opposed to the usually used datacentred approach marks the novelty of the present paper. ECIS constitutes a suitable paradigm for adaptive modeling of continuous dynamic processes and tracing the evolution of knowledge. The elements of evolution, such as inheritance and structure development are related to the knowledge and data pattern dynamics and are considered in the context of an individual system/model. Another novelty of this paper consists of the comparison at a conceptual level between the concept of models and knowledge captured by these models evolution and the well known paradigm of evolutionary computation. Although ECIS differs from the concept of evolutionary (genetic) computing, both paradigms heavily borrow from the same source – nature and human evolution. As the origin of knowledge, humans are the best model of an evolving intelligent system. Instead of considering the evolution of population of spices or genes as the evolutionary computation algorithms does the ECIS concentrate on the evolution of a single intelligent system. The aim is to develop the intelligence/knowledge of this system through an evolution using inheritance and modification, upgrade and reduction. This approach is also suitable for the integration of new data and existing models into new models that can be incrementally adapted to future incoming data. This powerful new concept has been recently introduced by the authors in a series of parallel works and is still under intensive development. It forms the conceptual basis for the development of the truly intelligent systems. Another specific of this paper includes bringing together the two working examples of ECIS, namely ECOS and EFS. The ideas are supported by illustrative examples (a synthetic non-linear function for the ECOS case and a benchmark problem of house price modelling from UCI repository for the case of EFS).

The past twenty years has seen a rapid growth of interest in stochastic search algorithms, particularly those inspired by natural processes in physics and biology. Impressive results have been demonstrated on complex practical optimisation problems and related search applications taken from a variety of fields, but the theoretical understanding of these algorithms remains weak. This results partly from the insufficient attention that has been paid to results showing certain fundamental limitations on universal search algorithms, including the so-called “No Free Lunch” Theorem. This paper extends these results and draws out some of their implications for the design of search algorithms, and for the construction of useful representations. The resulting insights focus attention on tailoring algorithms and representations to particular problem classes by exploiting domain knowledge. This highlights the fundamental importance of gaining a better theoretical grasp of the ways in which such knowledge may be systematically exploited as a major research agenda for the future.

The Natural Language Toolkit ( NLTK ) is widely used for teaching natural language processing to students majoring in linguistics or computer science. This paper describes the design ofNLTK , and reports on how it has been used effectively in classes that involve different mixes of linguistics and computer science students. We focus on three key issues: getting started with a course, delivering interactive demonstrations in the classroom, and organizing assignments and projects. In each case, we report on practical experience and make recommendations on how to useNLTK to maximum effect.


Question: EvoloPy: An open-access nature-inspired optimization library in Python.

This paper offers an extensive overview of a significant branch of evolutionary computation known as Evolution Strategies (ES), which has its origins in Germany dating back to the 1960s. The narrative begins with a historical perspective, followed by an exploration of the philosophical underpinnings that elucidate the rationale behind the design and implementation of ES. The fundamental ES algorithms are delineated, alongside the design philosophies for variation and selection operators. Theoretical foundations and contemporary research directions in ES are also discussed, providing a holistic understanding of the field and its future trajectories.

**Question**: An Improved Quantum-behaved Particle Swarm Optimization Algorithm

**Documents**: A Quantum-inspired Genetic Algorithm for Clustering
The paper proposes a Quantum-inspired Genetic Algorithm (QGA) for clustering problems. The algorithm uses quantum bits to encode the solutions, which allows for a compact representation of the search space. The QGA employs quantum gates to manipulate the population of solutions, thus providing a more efficient exploration of the search space compared to traditional genetic algorithms. The proposed algorithm is tested on several benchmark clustering problems and is shown to outperform existing methods.

**Similar Documents**: This work introduces a Quantum-inspired Genetic Algorithm (QGA) tailored for addressing clustering challenges. In contrast to conventional approaches, the QGA leverages quantum bits (qubits) to encode solutions, resulting in a more compact and expressive representation of the search space. The algorithm's innovation lies in its use of quantum gates to manipulate the population, thereby enabling a more effective exploration of potential solutions than traditional genetic algorithms. The QGA's efficacy is validated through its application to a series of benchmark clustering problems, where it demonstrates superior performance compared to established methods.

Theano is a linear algebra compiler that optimizes a user’s symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano’s performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.

Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission– fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.

Software evolution is widely recognized as an important and common phenomenon, whereby the system follows an ever-extending development trajectory w ith intermittent releases. Nevertheless there have been only few lifecycle models that attempt to por tray such evolution. We use the evolution of the Linux kernel as the basis for the formulation of such a model, integrating the progress in time with growth of the codebase, and differentiating bet ween development of new functionality and maintenance of production versions. A unique elem nt of the model is the sequence of activities involved in releasing new production versions, and how this has changed with the growth of Linux. In particular, the release follow-up phase before th forking of a new development version, which was prominent in early releases of production ve rsions, has been eliminated in favor of a concurrent merge window in the release of 2.6.x versions . We also show that a piecewise linear model with increasing slopes provides the best descr iption of the growth of Linux. The perpetual development model is used as a framework in which c ommonly recognized benefits of incremental and evolutionary development may be demonstra ted, nd to comment on issues such as architecture, conservation of familiarity, and failed p rojects. We suggest that this model and variants thereof may apply to many other projects in additio n to Linux.

Abstract: This paper generalises the golden section optimal search method to higher dimensional optimisation problem. The method is applicable to a strict quasi-convex function of N-variables over an N-dimensional hyper rectangle. An algorithm is proposed in N-dimension. The algorithm is illustrated graphically in two dimensions and verified through several test functions in higher dimension using MATLAB.

Operations managers and scholars in their search for fast and good solutions to real-world problems have applied genetic algorithms to many problems. While genetic algorithms are promising tools for problem solving, future research will benefit from a review of the problems that have been solved and the designs of the genetic algorithms used to solve them. This paper provides a review of the use of genetic algorithms to solve operations problems. Reviewed papers are classified according to the problems they solve. The basic design of each genetic algorithm is described, the shortcomings of the current research are discussed and directions for future research are suggested.

A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a natural choice for implementing indirect encodings, if only because nature itself uses this very process. Motivated by the development of embryos in nature, we define artificial embryogeny (AE) as the subdiscipline of evolutionary computation (EC) in which phenotypes undergo a developmental phase. An increasing number of AE systems are currently being developed, and a need has arisen for a principled approach to comparing and contrasting, and ultimately building, such systems. Thus, in this paper, we develop a principled taxonomy for AE. This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems. It also allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.

Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are vailable online xxx eywords: at algorithm haos etaheuristic lobal optimization utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks. © 2013 Elsevier B.V. All rights reserved. . Introduction Many design optimization problems are often highly nonlinar, which can typically have multiple modal optima, and it is thus ery challenging to solve such multimodal problems. To cope with his issue, global optimization algorithms are widely attempted, owever, traditional algorithms may not produce good results, and atest trends are to use new metaheuristic algorithms [1]. Metaeuristic techniques are well-known global optimization methods hat have been successfully applied in many real-world and comlex optimization problems [2,3]. These techniques attempt to imic natural phenomena or social behavior so as to generate etter solutions for optimization problem by using iterations and tochasticity [4]. They also try to use both intensification and iversification to achieve better search performance. Intensificaion typically searches around the current best solutions and selects he best candidate designs, while the diversification process allows he optimizer to explore the search space more efficiently, mostly y randomization [1]. In recent years, several novel metaheuristic algorithms have een proposed for global search. Such algorithms can increase he computational efficiency, solve larger problems, and implePlease cite this article in press as: A.H. Gandomi, X.-S. http://dx.doi.org/10.1016/j.jocs.2013.10.002 ent robust optimization codes [5]. For example, Xin-She Yang [6] ecently developed a promising metaheuristic algorithm, called bat lgorithm (BA). Preliminary studies suggest that the BA can have ∗ Corresponding author. Tel.: +44 2084112351. E-mail addresses: a.h.gandomi@gmail.com, ag72@uakron.edu (A.H. Gandomi), .yang@mdx.ac.uk (X.-S. Yang). 877-7503/$ – see front matter © 2013 Elsevier B.V. All rights reserved. ttp://dx.doi.org/10.1016/j.jocs.2013.10.002 superior performance over genetic algorithms and particle swarm optimization [6], and it can solve real world and engineering optimization problems [7–10]. On the other hand, recent advances in theories and applications of nonlinear dynamics, especially chaos, have drawn more attention in many fields [10]. One of these fields is the applications of chaos in optimization algorithms to replace certain algorithm-dependent parameters [11]. Previously, chaotic sequences have been used to tune parameters in metaheuristic optimization algorithms such as genetic algorithms [12], particle swarm optimization [13], harmony search [14], ant and bee colony optimization [15,16], imperialist competitive algorithm [17], firefly algorithm [18], and simulated annealing [19]. Such a combination of chaos with metaheuristics has shown some promise once the right set of chaotic maps are used. It is still not clear why the use of chaos in an algorithm to replace certain parameters may change the performance, however, empirical studies indeed indicate that chaos can have high-level of mixing capability, and thus it can be expected that when a fixed parameter is replaced by a chaotic map, the solutions generated may have higher mobility and diversity. For this reason, it may be useful to carry out more studies by introducing chaos to other, especially newer, metaheuristic algorithms. Therefore, one of the aims of this paper is to introduce chaos into the standard bat algorithm, and as a result, we propose a chaosbased bat algorithm (CBA). As different chaotic maps may lead to different behavior of the algorithm, we then have a set of chaosYang, Chaotic bat algorithm, J. Comput. Sci. (2013), based bat algorithms. In these algorithms, we use different chaotic systems to replace the parameters in BA. Thus different methods that use chaotic maps as potentially efficient alternatives to pseudorandom sequences have been proposed. In order to evaluate the

We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with—or outperform—other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code. The code is available at https://github.com/jmschrei/pomegranate

In this paper, a new two-population based global search algorithm, the Artificial Cooperative Search Algorithm (ACS), is introduced. ACS algorithm has been developed to be used in solving real-valued numerical optimization problems. For purposes of examining the success of ACS algorithm in solving numerical optimization problems, 91 benchmark problems that have different specifications were used in the detailed tests. The success of ACS algorithm in solving the related benchmark problems was compared to the successes obtained by PSO, SADE, CLPSO, BBO, CMA-ES, CK and DSA algorithms in solving the related benchmark problems by using Wilcoxon Signed-Rank Statistical Test with Bonferroni-Holm correction. The results obtained in the statistical analysis demonstrate that the success achieved by ACS algorithm in solving numerical optimization problems is better in comparison to the other computational intelligence algorithms used in this paper. 2012 Elsevier Inc. All rights reserved.

This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.


Question: EvoloPy: An open-source framework for nature-inspired optimization in Python.

This paper introduces the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a fast elitist multi-objective optimization technique that addresses the limitations of previous algorithms. Critics often targeted earlier multi-objective evolutionary algorithms for their high computational complexity, non-elitist nature, and the necessity of specifying a sharing parameter. NSGA-II, however, overcomes these challenges by implementing a rapid non-dominated sorting method with a computational complexity of O(mNlogN), where m is the number of objectives and N is the population size. Additionally, NSGA-II features a novel selection mechanism that merges the parent and offspring populations, selecting the top solutions based on both fitness and spread. Comparative simulations using five complex test problems demonstrate that NSGA-II significantly outperforms PAES—a competing elitist multi-objective evolutionary algorithm designed for generating diverse Pareto-optimal fronts—in terms of solution spread. The algorithm's low computational demands, elitist strategy, and parameter-less sharing mechanism are expected to make NSGA-II a popular choice for multi-objective optimization problems in the future.

**Question**: A Novel Hybrid PSO-DE Algorithm for Multi-Objective Optimization of Distributed Generation Planning

**Documents**: Multiobjective Optimization Using a Hybrid Particle Swarm and Evolutionary Algorithm
A hybrid particle swarm optimization (PSO) and evolutionary algorithm (EA) is proposed for solving multiobjective optimization problems. In the hybrid algorithm, a modified PSO is used to search for the Pareto-optimal solutions, while an EA is used to maintain diversity in the population. The EA is applied to the current population to generate a new population, which is then used to update the PSO. The hybrid algorithm is tested on several multiobjective optimization problems and is shown to be effective in finding a diverse set of Pareto-optimal solutions.

**Similar Documents**: This paper presents a novel hybrid approach combining particle swarm optimization (PSO) and evolutionary algorithms (EA) for tackling multiobjective optimization tasks. The proposed algorithm utilizes an enhanced PSO variant to locate Pareto-optimal solutions, while an EA ensures genetic diversity within the population. The EA is employed on the existing population to create a fresh set, which subsequently updates the PSO phase. This dual strategy is evaluated on a series of multiobjective optimization challenges, demonstrating its capability to discover a wide range of Pareto-optimal solutions, effectively balancing exploration and exploitation in the search space.

This paper introduces ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by pheromone they deposit on the edges of the TSP graph while building solutions. We study ACS by running experiments to understand its operation. The results show that ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs. Accepted for publication in the IEEE Transactions on Evolutionary Computation, Vol.1, No.1, 1997. In press.

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations at scale. Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms. However, building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models. Despite their increasing relevance as a component in NLP architectures, existing frameworks provide limited options in their ability to systematically build, parametrise, compare and evaluate different models. To answer this demand, this paper describes INDRA, a multi-lingual word embedding/distributional semantics framework which supports the creation, use and evaluation of word embedding models. In addition to the tool, INDRA also shares more than 65 pre-computed models in 14 languages.

This paper describes the use of genetic algorithms (GA) for solving the facility layout problem (FLP) within manufacturing systems’ design. The paper considers a specific heuristic layout planning method, known as the modified triangle method. This method seeks for an optimal layout solution based on the degrees of flows between workstations. The search for an optimal solution is extremely time-consuming and not suitable for larger systems; therefore we have developed a system based on evolutionary computation. Our paper presents a system, based on GA, and the results obtained regarding several numerical cases from the literature. We propose the usage of this system by presenting numerous advantages over other methods of solving FLP for problem representation and evolutionary computation for solution search. (Received in September 2012, accepted in June 2013. This paper was with the authors 2 months for 2 revisions.)

Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments : R, Python Scikit-learn, ∗Université de Toulouse – INSA, Institut de Mathématiques, UMR CNRS 5219 †Institut de Mathématiques, UMR CNRS 5219 ‡Université de Toulouse – UT3, Institut de Mathématiques, UMR CNRS 5219 1 ar X iv :1 60 9. 09 61 9v 1 [ st at .A P] 3 0 Se p 20 16 Spark MLlib on three public use cases : character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient for data munging and recommendation by collaborative filtering (non-negative factorization), current implementations of conventional learning methods (logistic regression, random forests) in MLlib or SparkML do not ou poorly compete habitual use of these methods (R, Python Scikit-learn) in an integrated or undistributed architecture.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). GECCO ’18 Companion, July 15--19, 2018, Kyoto, Japan © 2018 Copyright held by the owner/author(s). 978-1-4503-57647/18/07...$15.00 DOI https://doi.org/10.1145/3205651.3207859

A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.

While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framework for such problems by geometrically analyzing the multiobjective concepts of descent, diversity and convergence/optimality. It is shown that locally optimal, multi-objective descent direction can be calculated that maximally reduce all the objectives and a local sub-space also exists that is a basis for diversity updates. Local convergence of a point towards the optimal Pareto set is therefore assured. The concept of a population of points is also considered and it is shown that it can be used to locally increase the diversity of the population while still ensuring convergence and a method to extract the local directional information from the population is also described. The paper describes and introduces the basic theoretical concepts as well as demonstrating how they are used on simple test problems.

Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-EEL) or incrementally along evolution (On-EEL). Experiments on a set of benchmark problems show that Off-EEL outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.

Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.


Question: Recurrence networks - an innovative approach for nonlinear time series analysis.

The igraph: A Versatile Tool for Complex Network Analysis and Visualization

igraph emerges as a comprehensive software package designed to meet the multifaceted demands of complex network research, uniquely fulfilling a set of essential criteria. Unlike alternatives, igraph ensures efficient handling of large-scale graphs, a critical feature for researchers dealing with vast datasets. Its embeddability into sophisticated environments such as R and Python empowers users to leverage these powerful platforms for advanced analytics. Moreover, igraph stands out for its suitability in algorithm prototyping, a capability often compromised in user-friendly but restrictive "click & play" interfaces. Crucially, as an open-source and platform-independent tool, igraph democratizes access to cutting-edge network analysis, breaking down barriers to entry for researchers across different operating systems. Despite its advanced functionalities, igraph maintains an intuitive interface for interactive use, striking a balance between power and accessibility.

Nuclear marine apparatus is a huge complicated system, most of which equipments are of nonlin-earity, time varying, coupling and inexactness. Neuralnet is widely applied in nuclear fault diagnosis for its approaching any kinds of nonlinearity mapping. At present, BP neural net is used more widely, but the layers of the net and the neurones on each layer can not be delimited easily; such net may fall into the minimum point in the course of training. In this essay, PNN proves effective in diagnosing faults on nuclearmarine apparatus.

Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading time series prediction forecasting the future and understanding the past, you can take more advantages with limited budget.

The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon’s mapping SAMANN. Large scale applications are discussed: environmental data analysis, statistical analysis of curricula, comparison of schools, analysis of the economic and social conditions of countries, analysis of data on the fundus of eyes and analysis of physiological data on men’s health.

Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman's grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.

Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatiotemporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks (RNNs). We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.

Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.

As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have Historically the data pairs and logical, fashion transition while associative memories commonly? This organization of such as a, clear and algorithms search simulated annealing learning algorithm. After the utility of how to, discover form. More than three different levels of the cost function will. Dean pomerleau in reinforcement and they, discovered two chapters on. This view the application for handwriting recognition this approach by arrows originating.

This paper analyzes the behavior of stackaugmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, contextfree language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.

The problem of learning a general input-output relation using a layered neural network is discussed in a statistical framework. By imposing the consistency condition that the error minimization be equivalent to a likelihood maximization for training the network, the authors arrive at a Gibbs distribution on a canonical ensemble of networks with the same architecture. This statistical description enables them to evaluate the probability of a correct prediction of an independent example, after training the network on a given training set. The prediction probability is highly correlated with the generalization ability of the network, as measured outside the training set. This suggests a general and practical criterion for training layered networks by minimizing prediction errors. The authors demonstrate the utility of this criterion for selecting the optimal architecture in the continuity problem. As a theoretical application of the statistical formalism, they discuss the question of learning curves and estimate the sufficient training size needed for correct generalization, in a simple example.<<ETX>>

Pattern recognition is one of the traditional uses of neural networks. When trained with gradient-based learning methods, these networks can learn the classification of input data by example. An introduction to classifiers and gradient-based learning is given. It is shown how several perceptrons can be combined and trained gradient-based. Furthermore, an overview of convolutional neural networks, as well as a real-world example, are discussed.