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Evolutionary Algorithms

In: Fuzzy Evolutionary Computation

Author

Listed:
  • Zbigniew Michalewicz

    (University of North Carolina, Department of Computer Science
    Polish Academy of Sciences, Institute of Computer Science)

  • Robert Hinterding

    (Victoria University of Technology, Department of Computer and Mathematical Sciences)

  • Maciej Michalewicz

    (Polish Academy of Sciences, Institute of Computer Science)

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.

Suggested Citation

  • Zbigniew Michalewicz & Robert Hinterding & Maciej Michalewicz, 1997. "Evolutionary Algorithms," Springer Books, in: Witold Pedrycz (ed.), Fuzzy Evolutionary Computation, chapter 1, pages 3-31, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-6135-4_1
    DOI: 10.1007/978-1-4615-6135-4_1
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