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Optimisation in Time-Varying environments using Structured Genetic Algorithms
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BibTeX
@MISC{Dasgupta_optimisationin,
author = {Dipankar Dasgupta},
title = {Optimisation in Time-Varying environments using Structured Genetic Algorithms},
year = {}
}
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Abstract
This paper describes the application of Structured Genetic Algorithms (sGA) for tracking an optimum in time-varying environments. This genetic model incorporates redundancy in chromosomal encoding of the problem space and uses a gene activation mechanism for the phenotypic expression of its subspaces. These features allow multiple changes to occur simultaneously, in addition to usual mixing effects of genetic operators as in standard GAs. In adapting to nonstationary environments, the extra genetic material provides a source for maintaining variability within each individual, resulting in higher steady-state genotypic diversity even with phenotypic convergence of the population in different epoch. The paper presents some experimental results which demonstrate that sGAs can efficiently keep track of a moving optimum compared to existing genetic approaches. 1 Introduction Many real-world applications deal with situations in which the optimal criterion changes over time (typically with c...
Keyphrases
structured genetic algorithm time-varying environment nonstationary environment gene activation mechanism different epoch phenotypic convergence chromosomal encoding optimal criterion change phenotypic expression genetic model introduction many real-world application deal multiple change usual mixing effect genetic operator extra genetic material standard gas problem space genetic approach experimental result steady-state genotypic diversity
