Citation: L.M. Rocha and J. Kaur ."Genotype Editing and the Evolution of Regulation and Memory". Proceedings of the 9th European Conference on Artificial Life. Lecture Notes in Artificial Intelligence (LNAI), 4648: 63-73 (Springer-Verlag).
The pre-print is also available in Adobe Acrobat (.pdf) format only. Due to mathematical notation and graphics, only the abstract is presented here.
Our agent-based model of genotype editing is defined by two distinct genetic components: a coding portion encoding phenotypic solutions, and a non-coding portion used to edit the coding material. This set up leads to an indirect, stochastic genotype/phenotype mapping which captures essential aspects of RNA editing. We show that, in drastically changing environments, genotype editing leads to qualitatively different solutions from those obtained via evolutionary algorithms that only use coding genetic material. In particular, we show how genotype editing leads to the emergence of regulatory signals, and also to a resilient memory of a previous environment.
Keywords:RNA Editing, Genotype Editing, Regulation, Memory, Genetic Algorithms, agent-based modeling, coevolution, indirect genotype/phenotype mapping, dynamic environments, biologically-inspired computing.