Long-Term Progress and Behavior Complexification in Competitive Coevolution

The possibility of using competitive evolutionary algorithms to generate long-term progress is normally prevented by the convergence on limit cycle dynamics in which the evolving agents keep progressing against their current competitors by periodically rediscovering solutions adopted previously. This leads to local but not to global progress (i.e., progress against all possible competitors). We propose a new competitive algorithm that produces long-term global progress by identifying and filtering out opportunistic variations, that is, variations leading to progress against current competitors and retrogression against other competitors. The efficacy of the method is validated on the coevolution of predator and prey robots, a classic problem that has been used in related researches. The accumulation of global progress over many generations leads to effective solutions that involve the production of articulated behaviors. The complexity of the behavior displayed by the evolving robots increases across generations, although progress in performance is not always accompanied by behavior complexification.