A study of fitness distance correlation as a difficulty measure in genetic programming.
We present an approach to genetic programming difficulty based on a statistical study of program fitness landscapes. The fitness distance correlation is used as an indicator of problem hardness and we empirically show that such a statistic is adequate in nearly all cases studied here. However, fitness distance correlation has some known problems and these are investigated by constructing an artificial landscape for which the correlation gives contradictory indications. Although our results confirm the usefulness of fitness distance correlation, we point out its shortcomings and give some hints for improvement in assessing problem hardness in genetic programming.
Tomassini
Marco
M
Information Systems Department, Lausanne University, 1015 Lausanne, Switzerland. marco.tomassini@hec.unil.ch
Vanneschi
Leonardo
L
Collard
Philippe
P
Clergue
Manuel
M
D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005828:Genetics, Population; D006801:Humans; D008432:Mathematical Computing; D008433:Mathematics; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D012641:Selection, Genetic; D012984:Software