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 trailI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.