2003 •
Difficulty of unimodal and multimodal landscapes in genetic programming
Authors:
Leonardo Vanneschi, Marco Tomassini, Manuel Clergue, Philippe Collard
Abstract:
International audience; 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 d (...)
International audience; 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. (Read More)
Leonardo Vanneschi, Marco Tomassini, Manuel Clergue, Philippe Collard
Genetic and Evolutionary Computation — GECCO 2003 ·
2003
Artificial intelligence |
We have placed cookies on your device to help make this website and the services we offer better. By using this site, you agree to the use of cookies. Learn more