Fitness Distance Correlation in Structural Mutation Genetic Programming

Leonardo Vanneschi (1) , Marco Tomassini (1) , Philippe Collard (2) , Manuel Clergue (2)

1 Computer Science Institute
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

A new kind of mutation for genetic programming based on the structural distance operators for trees is presented in this paper. We firstly describe a new genetic programming process based on these operators (we call it structural mutation genetic programming). Then we use structural distance to calculate the fitness distance correlation coefficient and we show that this coefficient is a reasonable measure to express problem difficulty for structural mutation genetic programming for the considered set of problems, i.e. unimodal trap functions, royal trees and MAX problem.

Domaines: Intelligence artificielle [cs.AI]

Leonardo Vanneschi, Marco Tomassini, Philippe Collard, Manuel Clergue. Fitness Distance Correlation in Structural Mutation Genetic Programming. 6th European Conference, EuroGP 2003, 2003, Essex, United Kingdom. pp.455-464. ⟨hal-00159847⟩