Fitness distance correlation in structural mutation genetic programming

Authors:
Leonardo Vanneschi;Marco Tomassini;Philippe Collard;Manuel Clergue
Affiliations:
Computer Science Institute, University of Lausanne, Lausanne, Switzerland;Computer Science Institute, University of Lausanne, Lausanne, Switzerland;I3S Laboratory, University of Nice, Sophia Antipolis, France;I3S Laboratory, University of Nice, Sophia Antipolis, France
Venue:
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Year:
2003

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Abstract

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.