Abstract
We have proposed a design scheme for asynchronous multi-agent systemsbased on neural netwowk representation of the decision policy and itsoptimization with a real-coded GA. To obtain global optima with thisscheme, however, substantial computational resources are required. Weneed a new scheme that allows us to obtain acceptable local optima with much less resources. In this paper, we consider (1+1)-ES as analternative of the real-coded GA and discuss its potentials andlimitations through the application to the seesaw-balancing problem.
