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IEEE Transactions on Evolutionary Computation

Issue 2 • Date April 2004

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Displaying Results 1 - 16 of 16
  • 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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  • In Memoriam Don Dearholt

    Publication Year: 2004, Page(s):97 - 98
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  • Meta-Lamarckian learning in memetic algorithms

    Publication Year: 2004, Page(s):99 - 110
    Cited by:  Papers (330)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (920 KB) | HTML iconHTML

    Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigatin... View full abstract»

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  • A family of compact genetic algorithms for intrinsic evolvable hardware

    Publication Year: 2004, Page(s):111 - 126
    Cited by:  Papers (84)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (904 KB) | HTML iconHTML

    For many evolvable hardware applications, small size and power efficiency are critical design considerations. One manner in which significant memory, and thus, power and space savings can be realized in a hardware-based evolutionary algorithm is to represent populations of candidate solutions as probability vectors rather than as sets of bit strings. The compact genetic algorithm (CGA) is a probab... View full abstract»

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  • On the convergence of a class of estimation of distribution algorithms

    Publication Year: 2004, Page(s):127 - 136
    Cited by:  Papers (96)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (248 KB) | HTML iconHTML

    We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements. In this paper, we prove that: 1) if the distribution of the new elements matches that of the parent set exactly, the algorithms will converge t... View full abstract»

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  • Systematic integration of parameterized local search into evolutionary algorithms

    Publication Year: 2004, Page(s):137 - 155
    Cited by:  Papers (38)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (816 KB) | HTML iconHTML

    Application-specific, parameterized local search algorithms (PLSAs), in which optimization accuracy can be traded off with run time, arise naturally in many optimization contexts. We introduce a novel approach, called simulated heating, for systematically integrating parameterized local search into evolutionary algorithms (EAs). Using the framework of simulated heating, we investigate both static ... View full abstract»

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  • Quantum-inspired evolutionary algorithms with a new termination criterion, Hε gate, and two-phase scheme

    Publication Year: 2004, Page(s):156 - 169
    Cited by:  Papers (213)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (816 KB) | HTML iconHTML

    From recent research on combinatorial optimization of the knapsack problem, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms. To improve the performance of the QEA, this paper proposes research issues on QEA such as a termination criterion, a Q-gate, and a two-phase scheme, for a class of numerical and combinatorial optimization problems. A... View full abstract»

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  • Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions

    Publication Year: 2004, Page(s):170 - 182
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (392 KB) | HTML iconHTML

    This paper presents a rigorous running time analysis of evolutionary algorithms on pseudo-Boolean multiobjective optimization problems. We propose and analyze different population-based algorithms, the simple evolutionary multiobjective optimizer (SEMO), and two improved versions, fair evolutionary multiobjective optimizer (FEMO) and greedy evolutionary multiobjective optimizer (GEMO). The analysi... View full abstract»

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  • A novel approach to design classifiers using genetic programming

    Publication Year: 2004, Page(s):183 - 196
    Cited by:  Papers (70)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB) | HTML iconHTML

    We propose a new approach for designing classifiers for a c-class (c≥2) problem using genetic programming (GP). The proposed approach takes an integrated view of all classes when the GP evolves. A multitree representation of chromosomes is used. In this context, we propose a modified crossover operation and a new mutation operation that reduces the destructive nature of conventional genetic ope... View full abstract»

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  • Search for Editor-in-Chief for IEEE Transactions on NanoBioscience

    Publication Year: 2004, Page(s): 197
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  • IEEE Transactions on NanoBioscience

    Publication Year: 2004, Page(s): 198
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  • IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (2004)

    Publication Year: 2004, Page(s): 199
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  • IEEE Symposium on Computational Intelligence and Games

    Publication Year: 2004, Page(s): 200
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  • IEEE Neural Networks 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.
 

Full Aims & Scope

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