A survey of problem difficulty in genetic programming

Authors:
Leonardo Vanneschi;Marco Tomassini;Philippe Collard;Manuel Clergue
Affiliations:
Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.), University of Milano-Bicocca, Milan, Italy;Computer Systems Department, University of Lausanne, Lausanne, Switzerland;I3S Laboratory, University of Nice, Sophia Antipolis, France;I3S Laboratory, University of Nice, Sophia Antipolis, France
Venue:
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

This paper presents a study of fitness distance correlation and negative slope coefficient as measures of problem hardness for genetic programming. Advantages and drawbacks of both these measures are presented both from a theoretical and empirical point of view. Experiments have been performed on a set of well-known hand-tailored problems and “real-life-like” GP benchmarks.