The Influence of Binary Representations of Integers on thePerformance of Selectorecombinative Genetic Algorithms

When using representations for genetic algorithms (GAs) every optimization problem canbe separated into a genotype-phenotype and a phenotype-tness mapping. The genotypephenotypemapping is the used representation and the phenotype-tness mapping is the problemthat should be solved.This paper investigates how the use of dierent binary representations of integers inuencesthe performance of selectorecombinative GAs using only crossover and no mutation. It is shownthat the used representation strongly inuences the performance of GAs. The binary and grayencoding are two dierent, well known possibilities to assign bitstring genotypes to integer phenotypes.Focusing our investigation on these two encodings reveals that for the easy integerone-max problem selectorecombinative GAs perform better using binary encoding than usinggray encoding.[...]

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Year of publication:	2002-02-18
Authors:	Rothlauf, Franz
Institutions:	Universität <Mainz> / Lehrstuhl für Wirtschaftsinformatik und BWL
Subject:	Genetischer Algorithmus \| genetic algorithm \| Optimierungsproblem \| Phänotyp \| phenotype

1 Introduction
2 Binary Representations for Integer Optimization Problems
2.1 Separating Representations from Optimization Problems
2.2 An Integer Optimization Problem
2.3 Binary Representations of Integers
3 Performance of Crossover-based GAs using Binary Representations
3.1 Counting the Number of Binary Representations of Integers
3.2 Experimental Results
4 Performance of Binary and Gray Encoding
4.1 Experimental Results
4.2 Schemata Analysis for Binary and Gray encoding
5 Conclusions
References

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005868181