An adaptive surrogate-based optimization algorithm assisted by genetic operators sampling

This paper proposes an optimization algorithm that is applied to Black-box Problem, called Genetic Operator Sampling (GOS) adaptive surrogate-based optimization algorithm. Genetic operators produce candidate sample set. Cross-over operator is executed between any two of samples and mutation operator is executed only on present best sample. Then an assessment criterion, which is the product of the cross validation error of the candidate sample and the minimum distance between it and existing samples, is used to judge the adaptation of each sample. The candidate sample with largest product will be added to existing samples. GOS is illustrated on 1-D function in detail and is compared to EGO and MSE algorithm on three typical functions, the results validated the effectiveness of GOS algorithm.