Discovery of self-replicating structures using a genetic algorithm
Jason D. Lohn, James A. Reggia · 2002
Previous computational models of self-replication in cellular spaces have been manually designed, a very difficult and time-consuming process. This paper introduces the use of genetic algorithms to discover automata rules that govern emergent self-replicating processes. Given dynamically evolving automata, identification of effective fitness functions for self-replicating structures is a difficult task, and we give one solution to this problem. A model consisting of movable automata embedded in a cellular space is introduced and discussed in this context. A genetic algorithm using two fitness criteria was applied to automate rule discovery. After parameter tuning, 6 self-replicating structures consisting of 2, 3 and 4 automata were discovered over a course of 75 genetic algorithm runs. These results indicate that the fitness functions employed are effective and that genetic algorithms can be used to successfully discover rules for self-replicating structures.