There is noisy lunch: A study of noise in evolutionary optimization problems

Noise or uncertainty appear in many optimization processes when there is not a single measure of optimality or fitness but a random variable representing it. These kind of problems have been known for a long time, but there has been no investigation of the statistical distribution those random variables follow, assuming in most cases that it is distributed normally and, thus, it can be modelled via an additive or multiplicative noise on top of a non-noisy fitness. In this paper we will look at several uncertain optimization problems that have been addressed by means of Evolutionary Algorithms and prove that there is no single statistical model the evaluations of the fitness functions follow, being different not only from one problem to the next, but in different phases of the optimization in a single problem.