A similarity-based surrogate model for expensive evolutionary optimization with fixed budget of simulations

In order to find a satisfactory solution, genetic algorithms, in spite of their ability to solve difficult optimization problems, usually require a large number of fitness evaluations. When expensive simulations are required, using genetic algorithms as optimization tools can become prohibitive. In this paper we present a strategy for introducing surrogate models into genetic algorithms in order to enhance the quality of the final results, where a fixed budget of simulations is imposed. In this strategy, only a fraction of the population is evaluated by the exact function, thus allowing for more generations to evolve the population. The results obtained indicate that the proposed framework arises as an attractive alternative to improve the performance of the genetic algorithm within a fixed budget of expensive fitness evaluations.