- author = "Garnett Wilson and Malcolm Heywood",
- title = "Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP): A New Developmental Approach",
- booktitle = "Parallel Problem Solving from Nature - PPSN IX",
- year = "2006",
- editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao",
- volume = "4193",
- pages = "751--760",
- series = "LNCS",
- address = "Reykjavik, Iceland",
- publisher_address = "Berlin",
- month = "9-13 " # sep,
- publisher = "Springer-Verlag",
- ISBN = "3-540-38990-3",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1007/11844297_76",
- size = "10 pages",
- abstract = "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 customisable 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.",
-
notes = "PPSN-IX,
MAX and two box problems",
