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IEEE Transactions on Evolutionary Computation

Issue 1 • Date Apr 1998

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  • Bandit problems and the exploration/exploitation tradeoff

    Publication Year: 1998, Page(s):2 - 22
    Cited by:  Papers (38)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1584 KB)

    We explore the two-armed bandit with Gaussian payoffs as a theoretical model for optimization. The problem is formulated from a Bayesian perspective, and the optimal strategy for both one and two pulls is provided. We present regions of parameter space where a greedy strategy is provably optimal. We also compare the greedy and optimal strategies to one based on a genetic algorithm. In doing so, we... View full abstract»

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  • Solving constraint satisfaction problems using hybrid evolutionary search

    Publication Year: 1998, Page(s):23 - 33
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    We combine the concept of evolutionary search with the systematic search concepts of arc revision and hill climbing to form a hybrid system that quickly finds solutions to static and dynamic constraint satisfaction problems (CSPs). Furthermore, we present the results of two experiments. In the first experiment, we show that our evolutionary hybrid outperforms a well-known hill climber, the iterati... View full abstract»

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  • Evolutionary search for low autocorrelated binary sequences

    Publication Year: 1998, Page(s):34 - 39
    Cited by:  Papers (29)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (172 KB)

    The search for low autocorrelated binary sequences is a classical example of a discrete frustrated optimization problem. We demonstrate the efficiency of a class of evolutionary algorithms to tackle the problem. A suitable mutation operator using a preselection scheme is constructed, and the optimal parameters of the strategy are determined View full abstract»

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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