Document Type

Conference Proceeding




Faculty of Health, Engineering and Science


School of Computer and Security Science/Artificial Intelligence and Optimisation Research Group




This article was originally published as: While, L., & Hingston, P. (2013). Usefulness of infeasible solutions in evolutionary search: an empirical and mathematical study. Proceedings of the 2013 IEEE Congress on Evolutionary Computation. (pp. 1363-1370). Cancun, Mexico. IEEE. © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Original article available here


When evolutionary algorithms are used to solve constrained optimization problems, the question arises how best to deal with infeasible solutions in the search space. A recent theoretical analysis of two simple test problems argued that allowing infeasible solutions to persist in the population can either help or hinder the search process, depending on the structure of the fitness landscape. We report new empirical and mathematical analyses that provide a different interpretation of the previous theoretical predictions: that the important effect is on the probability of finding the global optimum, rather than on the time complexity of the algorithm. We also test a multi-objective approach to constraint-handling, and with an additional test problem we demonstrate the superiority of this multi-objective approach over the previous single-objective approaches.