Improving the Genetic Algorithm’s Performance when Using Transformation

Transformation is a biologically inspired genetic operator that, when incorporated in the standard Genetic Algorithm can promote diversity in the population. Previous work using this genetic operator in the domain of function optimization and combinatorial optimization showed that the premature convergence of the population is avoided. Furthermore, the solutions obtained were, in general, superior to the solutions achieved by the GA with standard 1-point, 2-point and uniform crossover. In this paper we present an extensive empirical study carried to determine the best parameter setting to use with transformation in order to enhance the GA’s performance. These parameters include the gene segment length, the replacement rate (percentage of individuals of the previous population used to update the gene segment pool), and the mutation and transformation rates.

Authors and Affiliations

1. Dept. of Informatics and Systems Engineering, Coimbra Polytechnic, Quinta da Nora, 3030, oimbra, Portugal

   Anabela Simões

2. Pinhal de Marrocos, Centre for Informatics and Systems of the Univ. of Coimbra, 3030, Coimbra, Portugal

   Anabela Simões & Ernesto Costa

Editors and Affiliations

1. Equipe Universitaire de Recherche en Informatique de Saint-Etienne (Groupe de Recherche de Roanne) Institut Universitaire de Technologie de Roanne, Université Jean Monnet, Saint-Etienne, France

   David W. Pearson

2. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

   Nigel C. Steele

3. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

   Rudolf F. Albrecht

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© 2003 Springer-Verlag Wien

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