Created by W.Langdon from gp-bibliography.bib Revision:1.6964
This study focuses on the effects of different fitness evaluation schemes on the types of genotypes and phenotypes that evolve. The evolutionary target is a simple numerical function. The genetic representation is in the form of a program (i.e., a functional representation, as in genetic programming). Many different programs can code for the same numerical function. In other words, there is a many-to-one mapping between genotype (the programs) and phenotypes. We compare fitness evaluation based on a large static set of problems and fitness evaluation based on small coevolving sets of problems. In the latter model very little information is presented to the evolving programs regarding the evolutionary target per evolutionary time step. In other words, the fitness evaluation is very sparse. Nevertheless the model produces correct solutions to the complete evolutionary target in about half of the simulations. The complete evaluation model, on the other hand, does not find correct solutions to the target in any of the simulations. More important, we find that sparse evaluated programs are better generalisable compared to the complete evaluated programs when they are evaluated on a much denser set of problems. In addition, the two evaluation schemes lead to programs that differ with respect to mutational stability; sparse evaluated programs are less stable than complete evaluated programs.",
p20 'the coevolutionary evaluation scheme works best if the population size is of the same order as the size of the complete set of problems.'
p20 'coevolving evaluation scheme needs much larger populations... computational cost nevertheless favours the coevolutionary...' p20 'random evaluation scheme' p20 'In the 2-D model the coevolving evaluation scheme is not much more efficient than the random evaluation scheme.' but see following text. p21 Figure 7. p23 'Thus the sparseness of the evaluation helps (rather than hinders) the search.'",
Genetic Programming entries for Ludo Pagie Paulien Hogeweg