For more information contact Luis Rocha at rocha@indiana.edu.
Last Modified: February 3, 2006
Citation: Rocha, L.M., A. Maguitman, C. Huang, J. Kaur, and S. Narayanan. [2006]."An Evolutionary Model of Genotype Editing". In: Artificial Life 10: Tenth International Conference on the Simulation and Synthesis of Living SystemsL.M.Rocha, L. Yaeger, M. Bedau, D. Floreano, R. Goldstone, and A. Vespignani (Eds.). MIT Press, pp. 105-111.
The full paper is available in Adobe Acrobat (.pdf) format only. Due to mathematical notation and graphics, only the abstract is presented here.
Abstract.
Evolutionary algorithms rarely deal with ontogenetic, noninherited alteration of genetic information because they are based on a simple, direct genotype-phenotype distinction. In contrast, in Nature several processes have been discovered which alter genetic information encoded in DNA before it is translated into amino-acid chains. Ontogenetically altered genetic information is not inherited but extensively used in regulation and development of phenotypes. An example of posttranscriptional alteration of gene-encoding sequences is the process of RNA Editing. Here we introduce a novel Agentbased model of genotype editing and a computational study of its evolutionary performance in static and dynamic environments. This model builds on our previous Genetic Algorithm with Edition, but presents a fundamentally novel architecture in which coding and non-coding genetic components are allowed to co-evolve. Our goal is twofold: (1) to study the role of RNA Editing regulation in the evolutionary process, and (2) to investigate the conditions under which genotype edition improves the performance of evolutionary algorithms. We show that edition allows evolving agents to perform better in several classes of fitness functions, both in static and dynamic environments.
Keywords:Genetic Algorithms, RNA Editing, Small RNA, Evolutionary Computation, Genotype Editing, Contextual Genetic Algorithms, Dynamic Environments, Genomics, Computational Biology, Optimization, Adaptive Behavior, Artificial Life
