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Fitness Clouds and Problem Hardness in Genetic ProgrammingLeonardo Vanneschi2, Manuel Clergue1, Philippe Collard1, Marco Tomassini2, and Sébastien Vérel1 1I3S Laboratory, University of Nice, Sophia Antipolis, France
2Information Systems Department, University of Lausanne, Lausanne, Switzerland Abstract. 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. LNCS 3103, p. 690 ff. lncs@springer.de
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