Finding Multiple Solutions with GA in Multimodal Problems

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Author(s): Marcos Gestal (University of A Coruña, Spain)and Mari Paz Gómez-Carracedo (University of A Coruña, Spain)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch098

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs) (Goldberg & Wang, 1989), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. The majority of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems (Harik, 1995) and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems.

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