Combining genetic algorithm with time-shuffling in order to evolve agent systems more efficiently

We have optimized a multi-agent system for all-to-all communication modeled in cellular automata. The agents' task is to solve the problem by communicating their initially mutually exclusive distributed information to all the other agents. We used a set of 20 environments (initial configurations), 10 with border, 10 with cyclic wrap-around to evolve the best behavior for agents with a uniform rule defined by a finite state machine. The state machine was evolved (1) directly by a genetic algorithm (GA) for all 20 environments and (2) indirectly by two separate GAs for the 10 environments with border and the 10 environments with wrap-around with a subsequent time-shuffling technique in order to integrate the good abilities from both of the separately evolved state machines. The time-shuffling technique alternates two state machines periodically. The results show that time-shuffling two separately evolved state machines is effective and much more efficient than the direct application of the GA.