Evolutionary Algorithms

Zbigniew Michalewicz, Robert Hinterding and Maciej Michalewicz
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Zbigniew Michalewicz: University of North Carolina, Department 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

Chapter 1.1 in Fuzzy Evolutionary Computation, 1997, pp 3-31 from Springer

Abstract: 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.

Keywords: Genetic Algorithm; Evolutionary Algorithm; Genetic Programming; Evolutionary Computation; Strategy Parameter (search for similar items in EconPapers)
Date: 1997
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DOI: 10.1007/978-1-4615-6135-4_1

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