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.

中文翻译：

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竞争协同进化中的长期进展和行为复杂化

使用竞争进化算法来产生长期进步的可能性通常被限制循环动力学的收敛所阻止，在这种情况下，进化代理通过定期重新发现以前采用的解决方案，不断与当前的竞争者对抗。这会导致本地而不是全球进步（即，与所有可能的竞争对手相比取得进步）。我们提出了一种新的竞争算法，它通过识别和过滤机会性变化来产生长期的全局进步，即导致与当前竞争对手相比进步的变化和对其他竞争对手的倒退。该方法的有效性在捕食者和猎物机器人的协同进化上得到了验证，这是一个已在相关研究中使用的经典问题。多代全球进步的积累导致了有效的解决方案，这些解决方案涉及产生明确的行为。不断进化的机器人所表现出的行为的复杂性随着世代的增加而增加，尽管性能的进步并不总是伴随着行为的复杂化。