Fitness approximation[1] aims to approximate the objective or fitness functions in evolutionary optimization by building up machine learning models based on data collected from numerical simulations or physical experiments.
[3] 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.
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.
Common approaches to constructing approximate models based on learning and interpolation from known fitness values of a small population include: Due to the limited number of training samples and high dimensionality encountered in engineering design optimization, constructing a globally valid approximate model remains difficult.
As a result, evolutionary algorithms using such approximate fitness functions may converge to local optima.