We applied an Island Model Genetic Algorithm (GA) to a Multi-Agent System (MAS) modeled in Cellular Automata (CA) in order to find the optimal behavior of the agents. The agents' task is to visit all free cells in a cellular grid containing obstacles as fast as possible. For this investigation we used a previously defined set of five different environments. The agents are controlled by a finite state machine with a restricted number of states and outputs (actions of the agents). Finite state machines with 4 to 7 states have been evolved by the GA. We compared the effectiveness (quality of solutions) and efficiency of the GA to an exhaustive search of all possible solutions. A special hardware (FPGA logic) has been used to enumerate and evaluate all 6-state finite state machines. The results show that the GA is much faster but almost as effective as the exhaustive search. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ediger, P., Hoffmann, R., & Halbach, M. (2009). Evolving 6-state Automata for optimal behaviors of creatures compared to exhaustive search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5717 LNCS, pp. 689–696). https://doi.org/10.1007/978-3-642-04772-5_89
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