Yuen Sizhe, Ezard Thomas H G, Sobey Adam J
Maritime Engineering, University of Southampton, Southampton SO17 1BJ, UK.
Ocean and Earth Science, National Oceanography Centre Southampton, European Way, University of Southampton, Southampton SO14 3ZH, UK.
R Soc Open Sci. 2023 May 10;10(5):221256. doi: 10.1098/rsos.221256. eCollection 2023 May.
Evolutionary computation is a group of biologically inspired algorithms used to solve complex optimization problems. It can be split into evolutionary algorithms, which take inspiration from genetic inheritance, and swarm intelligence algorithms, that take inspiration from cultural inheritance. However, much of the modern evolutionary literature remains relatively unexplored. To understand which evolutionary mechanisms have been considered, and which have been overlooked, this paper breaks down successful bioinspired algorithms under a contemporary biological framework based on the extended evolutionary synthesis, an extension of the classical, genetics focused, modern synthesis. Although the idea of the extended evolutionary synthesis has not been fully accepted in evolutionary theory, it presents many interesting concepts that could provide benefits to evolutionary computation. The analysis shows that Darwinism and the modern synthesis have been incorporated into evolutionary computation but the extended evolutionary synthesis has been broadly ignored beyond: cultural inheritance, incorporated in the sub-set of swarm intelligence algorithms, evolvability, through covariance matrix adaptation evolution strategy (CMA-ES), and multilevel selection, through multilevel selection genetic algorithm (MLSGA). The framework shows a gap in epigenetic inheritance for evolutionary computation, despite being a key building block in modern interpretations of evolution. This leaves a diverse range of biologically inspired mechanisms as low hanging fruit that should be explored further within evolutionary computation and illustrates the potential of epigenetic based approaches through the recent benchmarks in the literature.
进化计算是一组受生物启发的算法，用于解决复杂的优化问题。它可以分为从遗传继承中获取灵感的进化算法和从文化继承中获取灵感的群体智能算法。然而，现代进化文献的许多内容仍相对未被探索。为了了解哪些进化机制已被考虑，哪些被忽视，本文在基于扩展进化综合（经典的、以遗传学为重点的现代综合的扩展）的当代生物学框架下，对成功的受生物启发算法进行了分解。尽管扩展进化综合的理念在进化理论中尚未被完全接受，但它提出了许多有趣的概念，可能会给进化计算带来益处。分析表明，达尔文主义和现代综合已被纳入进化计算，但扩展进化综合在很大程度上被忽视了，除了：纳入群体智能算法子集中的文化继承、通过协方差矩阵自适应进化策略（CMA - ES）实现的可进化性，以及通过多级选择遗传算法（MLSGA）实现的多级选择。该框架显示出进化计算在表观遗传继承方面存在差距，尽管表观遗传继承是现代进化解释中的一个关键组成部分。这使得一系列受生物启发的机制成为进化计算中有待进一步探索的低垂果实，并通过文献中的最新基准说明了基于表观遗传学方法的潜力。
