A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation

Abstract

Constrained optimization problems are common in the sciences, engineering, and economics. Due to the growing complexity of the problems tackled, nature-inspired metaheuristics in general, and evolutionary algorithms in particular, are becoming increasingly popular. As move operators (recombination and mutation) are usually blind to the constraints, most metaheuristics must be equipped with a constraint handling technique. Although conceptually simple, penalty techniques usually require user-defined problem-dependent parameters, which often significantly impact the performance of a metaheuristic. A penalty technique is said to be adaptive when it automatically sets the values of all parameters involved using feedback from the search process without user intervention. This chapter presents a survey of the most relevant adaptive penalty techniques from the literature, identifies the main concepts used in the adaptation process, as well as observed shortcomings, and suggests further work in order to increase the understanding of such techniques.