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GECCO
2004
Springer

Fitness Clouds and Problem Hardness in Genetic Programming

9 years 7 months ago
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.
Leonardo Vanneschi, Manuel Clergue, Philippe Colla
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Leonardo Vanneschi, Manuel Clergue, Philippe Collard, Marco Tomassini, Sébastien Vérel
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