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IEEE Transactions on Evolutionary Computation

Issue 4 • Aug. 2006

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Displaying Results 1 - 15 of 15
  • Table of contents

    Publication Year: 2006, Page(s): c1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2006, Page(s): c2
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  • Guest Editorial Special Issue on Evolutionary Computation in the Presence of Uncertainty

    Publication Year: 2006, Page(s):377 - 379
    Cited by:  Papers (3)
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  • A general noise model and its effects on evolution strategy performance

    Publication Year: 2006, Page(s):380 - 391
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (470 KB) | HTML iconHTML

    Most studies concerned with the effects of noise on the performance of optimization strategies, in general, and on evolutionary approaches, in particular, have assumed a Gaussian noise model. However, practical optimization strategies frequently face situations where the noise is not Gaussian. Noise distributions may be skew or biased, and outliers may be present. The effects of non-Gaussian noise... View full abstract»

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  • Max-min surrogate-assisted evolutionary algorithm for robust design

    Publication Year: 2006, Page(s):392 - 404
    Cited by:  Papers (89)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (962 KB) | HTML iconHTML

    Solving design optimization problems using evolutionary algorithms has always been perceived as finding the optimal solution over the entire search space. However, the global optima may not always be the most desirable solution in many real-world engineering design problems. In practice, if the global optimal solution is very sensitive to uncertainties, for example, small changes in design variabl... View full abstract»

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  • Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation

    Publication Year: 2006, Page(s):405 - 420
    Cited by:  Papers (55)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (759 KB) | HTML iconHTML

    For many real-world optimization problems, the robustness of a solution is of great importance in addition to the solution's quality. By robustness, we mean that small deviations from the original design, e.g., due to manufacturing tolerances, should be tolerated without a severe loss of quality. One way to achieve that goal is to evaluate each solution under a number of different scenarios and us... View full abstract»

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  • Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

    Publication Year: 2006, Page(s):421 - 439
    Cited by:  Papers (126)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1391 KB) | HTML iconHTML

    This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information re... View full abstract»

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  • Locating and tracking multiple dynamic optima by a particle swarm model using speciation

    Publication Year: 2006, Page(s):440 - 458
    Cited by:  Papers (257)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (939 KB) | HTML iconHTML

    This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighborhood best values for solving multimodal optimization problems and for tracking multiple optima in a dynamic environment. In the proposed species-based particle swam optimization (SPSO), the swarm population is divided into species subpopulations based on their similarity. Each species is g... View full abstract»

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  • Multiswarms, exclusion, and anti-convergence in dynamic environments

    Publication Year: 2006, Page(s):459 - 472
    Cited by:  Papers (240)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (546 KB) | HTML iconHTML

    Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we explore new variants of particle swarm optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to split the population of particles into a set of interacting swarms. These swarms interact locally by an ... View full abstract»

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  • The First IEEE Symposium on Foundations of Computational Intelligence (FOCI'07)

    Publication Year: 2006, Page(s): 473
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  • Special issue on evolutionary computation for finance and economics

    Publication Year: 2006, Page(s): 474
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  • Put your technology leadership in writing

    Publication Year: 2006, Page(s): 475
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  • IEEE order form for reprints

    Publication Year: 2006, Page(s): 476
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2006, Page(s): c3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2006, 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.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Professor Kay Chen Tan (IEEE Fellow)
Department of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Email: kaytan@cityu.edu.hk