A comprehensive survey of fitness approximation in evolutionary computation

Abstract

Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evalu- ations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is compu- tationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approxi- mate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary com- putation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.