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.

进化计算受到生物进化机制的启发。随着算法的改进和计算资源的增加，进化计算已经发现了富有挑战性的实际问题的创新解决方案。本文评估了当今的进化计算如何与生物进化进行比较，以及它可能不足之处。考虑了少数公认的生物进化特征：开放性，组织结构的重大转变，中性和遗传漂移，多目标性，复杂的基因型到表型作图和共同进化。进化计算在某种程度上展示了其中许多，但是可以通过扩大可用计算范围并通过更仔细地模拟生物学来实现。特别是，进化计算在三个关键方面与生物学进化不同：它是基于小种群和强选择。它通常使用直接的基因型到表型的映射；并且它没有实现重大的组织过渡。这些缺点为未来的进化计算研究提出了路线图，并指出了我们对生物学如何发现主要转变的理解的差距。这些领域的进步可以导致进化计算接近生物学的复杂性和灵活性，并可以用作生物学过程的可执行模型。这些缺点为未来的进化计算研究提出了路线图，并指出了我们对生物学如何发现主要转变的理解的差距。这些领域的进步可以导致进化计算接近生物学的复杂性和灵活性，并可以用作生物学过程的可执行模型。这些缺点为未来的进化计算研究提出了路线图，并指出了我们对生物学如何发现主要转变的理解的差距。这些领域的进步可以导致进化计算接近生物学的复杂性和灵活性，并可以用作生物学过程的可执行模型。