Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming

Leonardo Vanneschi¹, Marco Tomassini¹, Manuel Clergue², and Philippe Collard²

¹Computer Science Institute
University of Lausanne
Lausanne, Switzerland

²I3S Laboratory
University of Nice
Sophia Antipolis, France

Abstract. This paper presents an original study of fitness distance correlation as a measure of problem difficulty in genetic programming. A new definition of distance, called structural distance, is used and suitable mutation operators for the program space are defined. The difficulty is studied for a number of problems, including, for the first time in GP, multimodal ones, both for the new hand-tailored mutation operators and standard crossover. Results are in agreement with empirical observations, thus confirming that fitness distance correlation can be considered a reasonable index of difficulty for genetic programming, at least for the set of problems studied here.

LNCS 2724, p. 1788 ff.

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