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Transposition versus Crossover: An Empirical Study

By Anabela Borges Simes, Ernesto Costa and Centre For Informatics and Centre For Informatics


Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this paper is to study one of these mechanisms: transposition. Transposition is a context-sensitive operator that promotes gene movement intra or inter chromosomes. This work presents an empirical study of the genetic algorithm performance, being the traditional crossover operator replaced by transposition. Such empirical study, based on an extensive set of test functions, shows that, under certain circumstances, transposition allows the GA to achieve higher quality solutions. 1 INTRODUCTION Genetic Algorithms (GA) are a search paradigm that applies ideas from evolutionary biology ..

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