Fitness Clouds and Problem Hardness in Genetic Programming

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 calculatedwithoutpriorknowledgeoftheglobaloptima.Thefitnesscloudisgenerated 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.

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Leonardo Vanneschi, Manuel Clergue, Philippe Collard, Marco Tomassini, Sébastien Vérel
• Keywords: GECCO 2004, Genetic Programming, Genetic Programming fitness, Negative Slope Coefficient