Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
An Adaptive Mapping for Developmental Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Genetic Programming and Evolvable Machines
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Developmental Genetic Programming (DGP) algorithms have been introduced where the search space for a problem is divided into genotypes and corresponding phenotypes that are connected by a mapping (or “genetic code”). Current implementations of this concept involve evolution of the mappings in addition to the traditional evolution of genotypes. We introduce the latest version of Probabilistic Adaptive Mapping DGP (PAM DGP), a robust and highly customizable algorithm that overcomes performance problems identified for the latest competing adaptive mapping algorithm. PAM DGP is then shown to outperform the competing algorithm on two non-trivial regression benchmarks.