A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms

Abstract

Evolutionary algorithms (EAs) used in complex optimization domains usually need to perform a large number of fitness function evaluations in order to get near-optimal solutions. In real world application domains such as engineering design problems, such evaluations can be extremely computationally expensive. In some extreme cases there is no clear definition of the fitness function or the fitness function is too ambiguous to be deterministically evaluated. It is therefore common to estimate or approximate the fitness. A popular method is to construct a so-called surrogate or meta-model, which can simulate the behavior of the original fitness function, but can be evaluated much faster. An interesting trend is to use multiple surrogates to gain better performance in fitness approximation. In this chapter, an up-to-date survey of fitness approximation applied in evolutionary algorithms is presented. The main focus areas are the methods of fitness approximation, the working styles of fitness approximation, and the management of the approximation during the optimization process. To conclude, some open questions in this area are discussed.