Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation Problems

Abstract

We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems. We find that UMDA and the Paired-Crossover Evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SubsetSum, Knapsack, and SetCover. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multiobjective problems (CountingOnesCountingZeros and a multiobjective formulation of SetCover). We compare several adaptations of UMDA for multiobjective problems with the Simple Evolutionary Multiobjective Optimiser (SEMO) and NSGA-II. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms.