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Evolutionary Computation, IEEE Transactions on

Issue 6 • Date Dec. 2006

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Displaying Results 1 - 16 of 16
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Page(s): C2
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  • Biasing Coevolutionary Search for Optimal Multiagent Behaviors

    Page(s): 629 - 645
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1914 KB) |  | HTML iconHTML  

    Cooperative coevolutionary algorithms (CEAs) offer great potential for concurrent multiagent learning domains and are of special utility to domains involving teams of multiple agents. Unfortunately, they also exhibit pathologies resulting from their game-theoretic nature, and these pathologies interfere with finding solutions that correspond to optimal collaborations of interacting agents. We address this problem by biasing a cooperative CEA in such a way that the fitness of an individual is based partly on the result of interactions with other individuals (as is usual), and partly on an estimate of the best possible reward for that individual if partnered with its optimal collaborator. We justify this idea using existing theoretical models of a relevant subclass of CEAs, demonstrate how to apply biasing in a way that is robust with respect to parameterization, and provide some experimental evidence to validate the biasing approach. We show that it is possible to bias coevolutionary methods to better search for optimal multiagent behaviors View full abstract»

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  • Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems

    Page(s): 646 - 657
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1042 KB) |  | HTML iconHTML  

    We describe an efficient technique for adapting control parameter settings associated with differential evolution (DE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE. We present an algorithm-a new version of the DE algorithm-for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems. The results show that our algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained View full abstract»

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  • A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization

    Page(s): 658 - 675
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1640 KB) |  | HTML iconHTML  

    A considerable number of constrained optimization evolutionary algorithms (COEAs) have been proposed due to increasing interest in solving constrained optimization problems (COPs) by evolutionary algorithms (EAs). In this paper, we first review existing COEAs. Then, a novel EA for constrained optimization is presented. In the process of population evolution, our algorithm is based on multiobjective optimization techniques, i.e., an individual in the parent population may be replaced if it is dominated by a nondominated individual in the offspring population. In addition, three models of a population-based algorithm-generator and an infeasible solution archiving and replacement mechanism are introduced. Furthermore, the simplex crossover is used as a recombination operator to enrich the exploration and exploitation abilities of the approach proposed. The new approach is tested on 13 well-known benchmark functions, and the empirical evidence suggests that it is robust, efficient, and generic when handling linear/nonlinear equality/inequality constraints. Compared with some other state-of-the-art algorithms, our algorithm remarkably outperforms them in terms of the best, mean, and worst objective function values and the standard deviations. It is noteworthy that our algorithm does not require the transformation of equality constraints into inequality constraints View full abstract»

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  • Evolutionary Design of Neural Network Architectures Using a Descriptive Encoding Language

    Page(s): 676 - 688
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (732 KB) |  | HTML iconHTML  

    Evolutionary algorithms are a promising approach to the automated design of artificial neural networks, but they require a compact and efficient genetic encoding scheme to represent repetitive and recurrent modules in networks. We present a problem-independent approach based on a human-readable and writable descriptive encoding using a high-level language. This encoding is based on developmental methods and a modular neural network paradigm. Here, we show that our approach works effectively by demonstrating that it can specify the search space compactly for "n-partition problems" and for sequence generation problems requiring recurrent networks, and that the evolved neural networks are parsimonious, modular, and capable of high-performance. We conclude that this approach based on high-level descriptive encoding can be useful in designing hierarchical, modular networks which may have recurrent connectivity, and is effective in describing the evolutionary search space, as well as the final neural networks resulting from the evolutionary process View full abstract»

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  • Clearance of Nonlinear Flight Control Laws Using Hybrid Evolutionary Optimization

    Page(s): 689 - 699
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (606 KB) |  | HTML iconHTML  

    The application of two evolutionary optimization methods, namely, differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling quality clearance criterion for a simulation model of a high-performance aircraft with a delta canard configuration and a full-authority flight control law. Hybrid versions of both algorithms, incorporating local gradient-based optimization, are also developed and evaluated. Statistical comparisons of computational cost and global convergence properties reveal the benefits of hybridization for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimization methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process View full abstract»

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  • Improved Heuristics for the Minimum Label Spanning Tree Problem

    Page(s): 700 - 703
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB) |  | HTML iconHTML  

    Given a connected, undirected graph G whose edges are labeled, the minimum label (or labeling) spanning tree (MLST) problem seeks a spanning tree on G with the minimum number of distinct labels. Maximum vertex covering algorithm (MVCA) is a well-known heuristic for the MLST problem. It is very fast and performs reasonably well. Recently, we developed a genetic algorithm (GA) for the MLST problem. The GA and MVCA are similarly fast but the GA outperforms the MVCA. In this paper, we present four modified versions of MVCA, as well as a modified GA. These modified procedures generate better results, but are more expensive computationally. The modified GA is the best performer with respect to both accuracy and running time View full abstract»

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  • Acknowledgment to Reviewers

    Page(s): 704 - 706
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  • Special issue on evolutionary computation for finance and economics

    Page(s): 707
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  • IEEE Congress on Evolutionary Computation

    Page(s): 708
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  • Special issue on evolutionary algorithms based on probabilistic models

    Page(s): 709
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  • Put your technology leadership in writing

    Page(s): 710
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  • 2006 Index

    Page(s): 711 - 716
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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Page(s): C4
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Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

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Meet Our Editors

Editor-in-Chief
Garrison W. Greenwood, Ph.D. P.E
Portland State University