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Factored Evolutionary Algorithms

Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function. In this paper, we give a formal definition of FEA algorithms and present empirical results related to their performance. One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize. For this reason, we present the results of experiments comparing the performance of different factor architectures on several standard applications for evolutionary algorithms. Additionally, we show that FEA’s performance is not restricted by the underlying optimization algorithm by creating FEA versions of hill climbing, particle swarm optimization, genetic algorithm, and differential evolution and comparing their performance to their single-population and cooperative coevolutionary counterparts. Continue reading

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Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes

Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show surprisingly good performance on widely used DTLZ and WFG test … Continue reading

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Heterogeneous Cooperative Co-Evolution Memetic Differential Evolution Algorithm for Big Data Optimization Problems

Evolutionary algorithms (EAs) have recently been suggested as a candidate for solving big data optimization problems that involve a very large number of variables and need to be analyzed in a short period of time. However, EAs face a scalability issue … Continue reading

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IEEE Transactions on Evolutionary Computation publication information

Provides a listing of the editorial board, current staff, committee members and society officers. Continue reading

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Table of contents

Presents the table of contents for this issue of the publication. Continue reading

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EvoStar panel on open-access publishing

Of possible interest to GPEM-affiliated folks who will be attending EvoStar: Jacqueline Heinerman has organized a lunchtime panel discussion session on Wednesday, on the topic of open access publishing, which will be sponsored by NWO, Netherlands Orga… Continue reading

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GPEM 18(1) is available

The first issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download. This is a special issue on Genetic Improvement, edited by Justyna Petke, and it also contains three book reviews. The complete contents are: “Edit… Continue reading

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IEEE Transactions on Evolutionary Computation information for authors

Presents institutional listings relating to this publication. Continue reading

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Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in… Continue reading

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IEEE World Congress on Computational Intelligence

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