School of Computer Engineering, Nanyang Technological University, 639798, Singapore.
Evol Comput. 2013 Summer;21(2):313-40. doi: 10.1162/EVCO_a_00079. Epub 2012 Jun 25.
To deal with complex optimization problems plagued with computationally expensive fitness functions, the use of surrogates to replace the original functions within the evolutionary framework is becoming a common practice. However, the appropriate datacentric approximation methodology to use for the construction of surrogate model would depend largely on the nature of the problem of interest, which varies from fitness landscape and state of the evolutionary search, to the characteristics of search algorithm used. This has given rise to the plethora of surrogate-assisted evolutionary frameworks proposed in the literature with ad hoc approximation/surrogate modeling methodologies considered. Since prior knowledge on the suitability of the data centric approximation methodology to use in surrogate-assisted evolutionary optimization is typically unavailable beforehand, this paper presents a novel evolutionary framework with the evolvability learning of surrogates (EvoLS) operating on multiple diverse approximation methodologies in the search. Further, in contrast to the common use of fitness prediction error as a criterion for the selection of surrogates, the concept of evolvability to indicate the productivity or suitability of an approximation methodology that brings about fitness improvement in the evolutionary search is introduced as the basis for adaptation. The backbone of the proposed EvoLS is a statistical learning scheme to determine the evolvability of each approximation methodology while the search progresses online. For each individual solution, the most productive approximation methodology is inferred, that is, the method with highest evolvability measure. Fitness improving surrogates are subsequently constructed for use within a trust-region enabled local search strategy, leading to the self-configuration of a surrogate-assisted memetic algorithm for solving computationally expensive problems. A numerical study of EvoLS on commonly used benchmark problems and a real-world computationally expensive aerodynamic car rear design problem highlights the efficacy of the proposed EvoLS in attaining reliable, high quality, and efficient performance under a limited computational budget.
为了解决计算成本高昂的适应度函数所困扰的复杂优化问题，在进化框架内使用替代物来替代原始函数已成为一种常见做法。然而，用于构建替代模型的数据中心近似方法的适当选择在很大程度上取决于所关注问题的性质，这些性质因适应度景观和进化搜索状态以及所使用的搜索算法的特点而异。这导致了文献中提出了大量的代理辅助进化框架，同时考虑了特定的近似/代理建模方法。由于在代理辅助进化优化中使用数据中心近似方法的适用性的先验知识通常是不可用的，因此本文提出了一种新颖的进化框架，其中使用了多个不同的近似方法来进行代理的可进化性学习（EvoLS）。此外，与常见的使用适应度预测误差作为选择代理的标准不同，本文引入了可进化性的概念，以指示近似方法的生产力或适用性，该概念可在进化搜索中带来适应度的提高，作为适应的基础。所提出的 EvoLS 的核心是一个统计学习方案，用于在搜索过程中确定每个近似方法的可进化性。对于每个个体解决方案，推断出最具生产力的近似方法，即具有最高可进化性度量的方法。随后，为使用信任区域启用的局部搜索策略构建适应度提高的替代物，从而导致用于解决计算成本高昂问题的代理辅助遗传算法的自我配置。EvoLS 在常用基准问题和实际计算成本高昂的汽车后设计问题上的数值研究强调了所提出的 EvoLS 在有限的计算预算下实现可靠、高质量和高效性能的有效性。
