Fitness Clouds and Problem Hardness in Genetic Programming

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

1 ISI - Institut des systèmes d'information
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

This paper presents an investigation of genetic programming fitness landscapes. We propose a new indicator of problem hardness for tree-based genetic programming, called negative slope coefficient, based on the concept of fitness cloud. The negative slope coefficient is a predictive measure, i.e. it can be calculated without prior knowledge of the global optima.The fitness cloud is generated via a sampling of individuals obtained with the Metropolis-Hastings method. The reliability of the negative slope coefficient is tested on a set of well known and representative genetic programming benchmarks, comprising the binomial-3 problem, the even parity problem and the artificial ant on the Santa Fe trail.

Mots clés: fitness landscape; genetic programming; evolutionary computation; fitness cloud; problem hardness

Domaines: Intelligence artificielle [cs.AI]

Leonardo Vanneschi, Manuel Clergue, Philippe Collard, Marco Tomassini, Sébastien Verel. Fitness Clouds and Problem Hardness in Genetic Programming. Genetic and Evolutionary Computation 2004, Jun 2004, Seattle, WA, United States. pp.690--701, ⟨10.1007/b98645⟩. ⟨hal-00160055⟩