Multiobjective Genetic Algorithms with Application to Control Engineering Problems (1995)
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BibTeX
@TECHREPORT{Fonseca95multiobjectivegenetic,
author = {Carlos Manuel Mira da Fonseca},
title = {Multiobjective Genetic Algorithms with Application to Control Engineering Problems},
institution = {},
year = {1995}
}
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Abstract
Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively
