An ecology-based evolutionary algorithm to evolve solutions to complex problems
An ecology-based evolutionary algorithm to evolve solutions to complex problems
复制标题
基于生态学的进化算法，用于进化复杂问题的解决方案
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Charles Ofria
中科院分区：
文献类型：
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作者：
Sherri Goings;Heather Goldsby;B. Cheng;Charles Ofria
进化算法在为现实世界的问题提供新颖的解决方案方面显示出巨大的希望，但这些解决方案的复杂性是有限的，不像自然世界中发生的明显开放的进化。在某种程度上，大自然通过生态动态来克服这些复杂的障碍，这些动态产生了多种多样的原材料，供进化的基础上发展。作者之前介绍了Eco-EA，这是一种进化算法，它整合了这些自然生态动态，以促进和维持进化种群的多样性。在这里，我们将Eco-EA应用于现实世界的软件工程问题，即用于洪水监测的遥感网络中部署节点的演化行为模型。我们展示了Eco-EA比传统EA更快地进化出良好的行为模型，比传统EA生成更多样化的模型套件，并且创建的模型本身比传统EA创建的模型更具可进化性。
Evolutionary algorithms have shown great promise in evolving novel solutions to real-world problems, but the complexity of those solutions is limited, unlike the apparently open-ended evolution that occurs in the natural world. In part, nature surmounts these complexity barriers with ecological dynamics that generate a diverse array of raw materials for evolution to build upon. The authors previously introduced Eco-EA, an evolutionary algorithm that integrates these natural ecological dynamics to promote and maintain diversity in the evolving population. Here, we apply the Eco-EA to the real-world software engineering problem of evolving behavioral models for deployed nodes in a remote sensor network for flood monitoring. We show that the Eco-EA evolves good behavioral models faster than a traditional EA, generates a more diverse suite of models than a traditional EA, and creates models that are themselves more evolvable than those created by a traditional EA.