Long-Term Progress and Behavior Complexification in Competitive Co-Evolution
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The possibility to use 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 over and over again. This leads to local but not to global progress, i.e. progress against all possible competitors. We propose a new competitive algorithm capable of leading to long term global progress thanks to its ability to identify and filter out opportunistic variations, i.e. variations leading to progress against current competitors and retrogression against other competitors. The efficacy of the method is validated on the co-evolution of predator and prey robots, a classic scenario that has been used in other related researches. The accumulation of global progress over many generation leads to effective solutions that involve the production of rather articulated behaviors. The complexity of the behavior displayed by the evolving robots tend to increase across generation although progresses in performance are not always accompanied by behavior complexification.
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