Studying the Evolvability of of Self-Encoding Genotype-Phenotype Maps

Andrew Webb, Joshua Knowles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

We introduce a model of reproduction in which the genotypephenotype (G-P) map is able to evolve. In this model, Each organism implements a G-P map, determining how the organism is encoded in its genome. Crucially, it also determines how the G-P map itself is encoded. We call these maps ‘self-encoding’. We relate this model to recent artificial life research, and back to the seminal work of John von Neumann. We simulate populations of organisms that have as their genome and G-P map the axiom and production rules of an L-system. The populations are given the task of optimizing a dynamic fitness function. Our purpose is to study whether the self-encoding property has any effect on the evolution of evolvability, and to look for other factors that lead to the evolution of G-P maps that confer evolvability. We find that evolvability does evolve, but only when we add constraints to the model.
Original languageEnglish
Title of host publication Artificial Life 14
Subtitle of host publicationProceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems
EditorsHiroki Sayama, John Rieffel, Sebastian Risi, René Doursat, Hod Lipson
PublisherMIT Press
Pages79-86
DOIs
Publication statusPublished - 30 Jul 2014
EventFourteenth International Conference on the Synthesis and Simulation of Living Systems - New York, United States
Duration: 30 Jul 20142 Aug 2014

Conference

ConferenceFourteenth International Conference on the Synthesis and Simulation of Living Systems
Country/TerritoryUnited States
CityNew York
Period30/07/142/08/14

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