Competitive Coevolution through Evolutionary Complexification
Competitive Coevolution through Evolutionary Complexification
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DOI：
10.1613/jair.1338
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发表时间：
2011-06
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影响因子：
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通讯作者：
Kenneth O. Stanley;R. Miikkulainen
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文献类型：
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作者：
Kenneth O. Stanley;R. Miikkulainen
机器学习的两个主要目标是发现和改进复杂问题的解决方案。在本文中，我们认为，复杂化，即通过增加新的结构，解决方案的增量阐述，实现这两个目标。我们通过增强拓扑的神经进化（NEAT）方法展示了复杂化的力量，该方法发展了越来越复杂的神经网络架构。NEAT适用于一个开放式的共同进化机器人决斗域，机器人控制器竞争头对头。由于机器人决斗领域支持广泛的策略，而且由于共同进化受益于不断升级的军备竞赛，因此它可以作为研究复杂化的合适试验平台。当与具有固定结构的网络的演化相比时，复杂化演化发现了明显更复杂的策略。结果表明，为了发现和改进复杂的解决方案，进化和搜索一般来说，应该允许复杂化以及优化。
Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.