A biological perspective on evolutionary computation
A biological perspective on evolutionary computation
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DOI：
10.1038/s42256-020-00278-8
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发表时间：
2021-01-01
影响因子：
23.8
通讯作者：
Forrest, Stephanie
中科院分区：
文献类型：
--
作者：
Miikkulainen, Risto;Forrest, Stephanie
进化计算的灵感来自生物进化的机制。随着算法的改进和计算资源的增加，进化计算发现了针对挑战的实际问题的创造性和创新解决方案。本文评估了当今的进化计算与生物学进化的相比以及如何降低。考虑了少数经过良好接受的生物进化特征：开放性，组织结构的主要过渡，中立性和遗传漂移，多目标，复杂的基因型到表型映射和共同进化。进化计算在某种程度上表现出了许多其中的许多，但是通过使用可用的计算并更仔细地模拟生物学可以实现更多的计算。特别是，进化计算在三个关键方面与生物进化不同：它基于少数人群和强烈的选择；它通常使用直接的基因型到表型映射；而且它没有实现主要的组织过渡。这些缺点暗示了未来进化计算研究的路线图，并指出了我们对生物学如何发现主要过渡的理解的差距。这些领域的进步可能会导致进化计算，从而接近生物学的复杂性和灵活性，并可以作为生物过程的可执行模型。
Evolutionary computation is inspired by the mechanisms of biological evolution. With algorithmic improvements and increasing computing resources, evolutionary computation has discovered creative and innovative solutions to challenging practical problems. This paper evaluates how today's evolutionary computation compares to biological evolution and how it may fall short. A small number of well-accepted characteristics of biological evolution are considered: openendedness, major transitions in organizational structure, neutrality and genetic drift, multi-objectivity, complex genotype-to-phenotype mappings and co-evolution. Evolutionary computation exhibits many of these to some extent but more can be achieved by scaling up with available computing and by emulating biology more carefully. In particular, evolutionary computation diverges from biological evolution in three key respects: it is based on small populations and strong selection; it typically uses direct genotype-to-phenotype mappings; and it does not achieve major organizational transitions. These shortcomings suggest a roadmap for future evolutionary computation research, and point to gaps in our understanding of how biology discovers major transitions. Advances in these areas can lead to evolutionary computation that approaches the complexity and flexibility of biology, and can serve as an executable model of biological processes.