A Meta-Evolutionary Algorithm for Co-evolving Genotypes and Genotype / Phenotype Maps

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{gaylinn:2024:GECCOcomp,

• author = "Nathan Gaylinn and Joshua Bongard",
• title = "A {Meta-Evolutionary} Algorithm for Co-evolving Genotypes and Genotype / Phenotype Maps",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Alberto Moraglio and James McDermott",
• pages = "467--470",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, evolution strategies, gp map, generative encoding, cellular automata, game of life, gol, evolvability, diversity, exploration, CPPNs, development, General Evolutionary Computation, Hybrids: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654259",
• size = "4 pages",
• abstract = "Evolutionary computation (EC) is often used to automatically discover solutions to optimization problems. It is valued because it allows the programmer to intuitively design a search space to fit a task, and because it is a relatively open-ended search process that favors diversity and unanticipated solutions that might be missed with gradient-based methods. Traditionally, the programmer decides on a fixed search strategy a priori, often by designing a specialized mapping from genotype to phenotype (GP map). Unfortunately, this can introduce bias and undermine the open-endedness of EC. Evolved GP maps can mitigate these concerns by automatically discovering efficient search spaces that improve evolvability. However, most research into evolved GP maps emphasizes convergence rate to a fit solution, or rate of recovery after a change in conditions. Here, we frame EC as a search over search strategies rather than a search for fit solutions. We demonstrate that a single meta-evolutionary algorithm with an evolved generative GP map can find better solutions to multiple fitness functions in the domain of 2D cellular automata than a traditional evolutionary algorithm. In the future, we hope these results will further the understanding of evolvability, its relationship to diversity, and the exploratory power of evolved GP maps.",
• notes = "is this GP? p468 'To render a phentoype, we walk the tree of operations in the GP map' GECCO-2024 GECH A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Nathan Gaylinn Josh C Bongard

Citations