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

Vanneschi, L., Clergue, M., Collard, R., Tomassini, M., Verel, S. (2004). Fitness clouds and problem hardness in genetic programming. In Genetic and Evolutionary Computation Conference Seattle,WA, USA, June 26-30, 2004 Proceedings, Part II (pp.690-701). Berlin : Springer [10.1007/978-3-540-24855-2_76].

Fitness clouds and problem hardness in genetic programming

Vanneschi, L;
2004

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
paper
fitness, clouds, problem, hardness, genetic, programming
English
Genetic and Evolutionary Computation Conference, GECCO'04, Seattle, Washington, USA, 26-30 June 2004
2004
Deb, K; Poli, R; Banzhaf, W; Beyer, HG; Burke, E; Darwen, P; Dasgupta, D; Floreano, D; Foster, O; Harman, M; Holland, O; Lanzi, PL; Spector, L; Tettamanzi, A; Thierens, D; Tyrrell, A
Genetic and Evolutionary Computation Conference Seattle,WA, USA, June 26-30, 2004 Proceedings, Part II
978-3-540-22343-6
2004
3103
690
701
none
Vanneschi, L., Clergue, M., Collard, R., Tomassini, M., Verel, S. (2004). Fitness clouds and problem hardness in genetic programming. In Genetic and Evolutionary Computation Conference Seattle,WA, USA, June 26-30, 2004 Proceedings, Part II (pp.690-701). Berlin : Springer [10.1007/978-3-540-24855-2_76].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/13553
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