Efficiency improvement of imitation operator in multi-agent control model based on Cartesian Genetic Programming

Abstract

In this paper, we focus on evolutionary optimization of multi-agent behavior. In our previous work, we have proposed a multi-agent control model based on Cartesian Genetic Programming (CGP). In CGP, each individual is represented by a graph-structural program. The CGP has a characteristics that each individual has multiple output nodes. Therefore, by assigning the outputs to respective agents, we can control multiple agents by an individual. The method enables multiple agents to not only take different actions according to their own roles but also share sub-programs if the same behavior is needed for solving problems. In addition, a new genetic operator for multi-agent control, imitation operator, has been proposed to facilitate the grouping of agents. An agent selects another agent at random for imitating the behavior. However, if the number of agents increases, the appropriate agent cannot always be selected for imitation. Therefore, in this paper, we propose a modified imitation operator for selecting useful agent. We applied our method to a food foraging problem. The experimental results showed that the performance of our method is superior to those of the conventional models.