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IEEE Transactions on Evolutionary Computation

Issue 5 • Date Oct. 2004

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Displaying Results 1 - 11 of 11
  • Table of contents

    Publication Year: 2004, Page(s): c1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2004, Page(s): c2
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  • Dynamic multiobjective optimization problems: test cases, approximations, and applications

    Publication Year: 2004, Page(s):425 - 442
    Cited by:  Papers (137)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2240 KB) | HTML iconHTML

    After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and ... View full abstract»

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  • The preservation of favored building blocks in the struggle for fitness: the puzzle algorithm

    Publication Year: 2004, Page(s):443 - 455
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (608 KB) | HTML iconHTML

    The shortest common superstring (SCS) problem, known to be NP-complete, seeks the shortest string that contains all strings from a given set. In this paper, we present a novel coevolutionary algorithm-the Puzzle Algorithm-where a population of building blocks coevolves alongside a population of solutions. We show experimentally that our novel algorithm outperforms a standard genetic algorithm (GA)... View full abstract»

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  • A robust stochastic genetic algorithm (StGA) for global numerical optimization

    Publication Year: 2004, Page(s):456 - 470
    Cited by:  Papers (74)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1328 KB) | HTML iconHTML

    Many real-life problems can be formulated as numerical optimization of certain objective functions. However, often an objective function possesses numerous local optima, which could trap an algorithm from moving toward the desired global solution. Evolutionary algorithms (EAs) have emerged to enable global optimization; however, at the present stage, EAs are basically limited to solving small-scal... View full abstract»

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  • An evolutionary approach to pattern-based time series segmentation

    Publication Year: 2004, Page(s):471 - 489
    Cited by:  Papers (42)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1312 KB) | HTML iconHTML

    Time series data, due to their numerical and continuous nature, are difficult to process, analyze, and mine. However, these tasks become easier when the data can be transformed into meaningful symbols. Most recent works on time series only address how to identify a given pattern from a time series and do not consider the problem of identifying a suitable set of time points for segmenting the time ... View full abstract»

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  • Local function approximation in evolutionary algorithms for the optimization of costly functions

    Publication Year: 2004, Page(s):490 - 505
    Cited by:  Papers (65)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2992 KB) | HTML iconHTML

    We develop an approach for the optimization of continuous costly functions that uses a space-filling experimental design and local function approximation to reduce the number of function evaluations in an evolutionary algorithm. Our approach is to estimate the objective function value of an offspring by fitting a function approximation model over the k nearest previously evaluated points, where k=... View full abstract»

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  • Analysis of fitness functions for electron-beam lithography simulation and evolutionary optimization

    Publication Year: 2004, Page(s):506 - 511
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (720 KB) | HTML iconHTML

    In a previous paper, an electron-beam lithography simulation and optimization tool based on a genetic algorithm was presented. The fitness function was defined as the inverse value of the Euclidian distance between a resampled computed resist profile and its targeted counterpart. In this letter, this previously proposed fitness function is analyzed and its limitations are presented. It is shown th... View full abstract»

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  • IEEE Symposium on Computational Intelligence and Games

    Publication Year: 2004, Page(s): 512
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2004, Page(s): c3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2004, 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

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan