Probabilistic (Genotype) Adaptive Mapping Combinations for Developmental Genetic Programming
Garnett Wilson, Malcolm Iain Heywood · 2006
In development genetic programming (DGP) approaches where the search space is divided into genotypes and phenotypes, a mapping (or "genetic code") is needed to connect the two spaces. This model has subsequently been extended so that mappings evolve, and recently an implementation was proposed that co-evolves a genotype population and a population of adaptive mappings. Here, the authors identify and investigate performance obstacles for this recent implementation. They then introduce a new probabilistic adaptive mapping DGP that avoids those performance problems and explores a greater search space of genotype-mapping combinations without significant computational expense. The algorithm is shown to be more robust and to outperform the comparison adaptive mapping algorithm on challenging settings of the chosen test benchmark.