Fitness approximation

In function optimization, fitness approximation is a method for decreasing the number of fitness function evaluations to reach a target solution. It belongs to the general class of evolutionary computation or artificial evolution methodologies. In function optimization, fitness approximation is a method for decreasing the number of fitness function evaluations to reach a target solution. It belongs to the general class of evolutionary computation or artificial evolution methodologies. In many real-world optimization problems including engineering problems, the number of fitness function evaluations needed to obtain a good solution dominates the optimization cost. In order to obtain efficient optimization algorithms, it is crucial to use prior information gained during the optimization process. Conceptually, a natural approach to utilizing the known prior information is building a model of the fitness function to assist in the selection of candidate solutions for evaluation. A variety of techniques for constructing such a model, often also referred to as surrogates, metamodels or approximation models – for computationally expensive optimization problems have been considered.