Investigation of Genome Parameters and Sub-Transitions to Guide Evolution of Artificial Cellular Organisms
Artificial multi-cellular organisms develop from a single zygote to complex morphologies, following the instructions encoded in their genomes. Small genome mutations can result in very different developed phenotypes. In this paper we investigate how to exploit genotype information in order to guide evolution towards favorable areas of the phenotype solution space, where the sought emergent behavior is more likely to be found. Lambda genome parameter, with its ability to discriminate different developmental behaviors, is incorporated into the fitness function and used as a discriminating factor for genetic distance, to keep resulting phenotype’s developmental behavior close by and encourage beneficial mutations that yield adaptive evolution. Genome activation patterns are detected and grouped into genome parameter sub-transitions. Different sub-transitions are investigated as simple genome parameters, or composed to integrate several genome properties into a more exhaustive composite parameter. The experimental model used herein is based on 2-dimensional cellular automata.
KeywordsArtificial Development Evolution Complexity Emergence Cellular Automata
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- 2.Bar-Yam, Y.: Dynamics of complex systems. Studies in Nonlinearity, p. 864. Westview Press (1997)Google Scholar
- 5.Glover, F., Kochenberg, G.A.: Handbook of metaheuristics. International Series on Operations Research and Management Science, p. 570. Springer (2003)Google Scholar
- 6.Langton, C.G.: Computation at the edge of chaos: phase transitions and emergant computation. In: Forrest, S. (ed.) Emergent Computation, pp. 12–37. MIT Press (1991)Google Scholar
- 7.Tufte, G., Nichele, S.: On the correlations between developmental diversity and genomic composition. In: 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 1507–1514. ACM (2011)Google Scholar
- 9.de Oliveira, G., de Oliveira, P., Omar, N.: Definition and application of a five-parameter characterization of one-dimensional cellular automata rule space. Artificial Life 7, 277–301 (2001)Google Scholar
- 10.de Oliveira, G., de Oliveira, P., Omar, N.: Guidelines for dynamics-based parameterization of one-dimensional cellular automata rule space. Complexity 6(2) (2001)Google Scholar
- 11.Kowaliw, T.: Measures of complexity for artificial embryogeny. In: GECCO 2008, pp. 843–850. ACM (2008)Google Scholar
- 12.Rothlauf, F.: Locality, distance distortion, and binary representations of integers. Working Paper 11/2003. University of MannheimGoogle Scholar