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Evolutionary Algorithms

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Fuzzy Evolutionary Computation

Abstract

Evolutionary algorithms (EAs), which are based on a powerful principle of evolution: survival of the fittest, and which model some natural phenomena: genetic inheritance and Darwinian strife for survival, constitute an interesting category of modern heuristic search. This introductory article presents the main paradigms of ECs and discusses other (hybrid) methods of evolutionary computation. We also discuss the ways an evolutionary algorithm can be tuned to the problem while it is solving the problem, as this can dramatically increase efficiency. ECs have been widely used in science and engineering for solving complex problems. An important goal of research on ECs is to understand the class of problems for which these algorithms are most suited, and, in particular, the class of problems on which they outperform other search algorithms.

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Authors and Affiliations

  1. Department of Computer Science, University of North Carolina, Charlotte, NC, 28223, USA

    Zbigniew Michalewicz

  2. Institute of Computer Science, Polish Academy of Sciences, 01-237, Warsaw, Poland

    Zbigniew Michalewicz & Maciej Michalewicz

  3. Department of Computer and Mathematical Sciences, Victoria University of Technology, PO Box 14428 MMC, Melbourne, 3000, Australia

    Robert Hinterding

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  1. Zbigniew Michalewicz
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  2. Robert Hinterding
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  3. Maciej Michalewicz
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Editors and Affiliations

  1. The University of Manitoba, Winnipeg, Manitoba, Canada

    Witold Pedrycz

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Michalewicz, Z., Hinterding, R., Michalewicz, M. (1997). Evolutionary Algorithms. In: Pedrycz, W. (eds) Fuzzy Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6135-4_1

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