Towards explainable metaheuristics: Feature extraction from trajectory mining

作     者：Fyvie, Martin Mccall, John A. W. Christie, Lee A. Brownlee, Alexander E. I. Singh, Manjinder 

作者机构：Robert Gordon Univ Natl Subsea Ctr Aberdeen Scotland Univ Stirling Div Comp Sci & Math Stirling Scotland 

出 版 物：《EXPERT SYSTEMS》 (Expert Syst)

年 卷 期：2025年第42卷第1期

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：BT Group Data Lab 

主　　题：evolutionary algorithms explainability PCA population diversity 

摘      要：Explaining the decisions made by population-based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that increasingly require end-user input and confirmation, the need to explain the internal decisions being made has grown. In this article, we present our approach to the extraction of explanation supporting features using trajectory mining. This is achieved through the application of principal components analysis techniques to identify new methods of tracking population diversity changes post-runtime. The algorithm search trajectories were generated by solving a set of benchmark problems with a genetic algorithm and a univariate estimation of distribution algorithm and retaining all visited candidate solutions which were then projected to a lower dimensional sub-space. We also varied the selection pressure placed on high fitness solutions by altering the selection operators. Our results show that metrics derived from the projected sub-space algorithm search trajectories are capable of capturing key learning steps and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients. A comparative study of variable importance rankings derived from a surrogate model built on the same dataset was also performed. The results show that both approaches are capable of identifying key features regarding variable interactions and their influence on fitness in a complimentary fashion.