A Surrogate-Based Two-Level Genetic Algorithm Optimization Through Wavelet Transform

Abstract

Despite the surrogate-based two-level algorithms that have been proposed for accelerating the optimization procedures, it may be still expensive for large problems. Therefore, this paper proposes the exploration of the approximation characteristics of the wavelet functions to define a coarse subspace for this kind of approach with relatively few float point operations. The wavelet transform is used to create the coarse model in a two-level genetic algorithm (GA), which is applied to a set of benchmark test problems. Although the coarse model is simpler and less accurate than the fine model, it behaves similarly to this last one and the original function. Moreover, the approach prevented the convergence to local minima whenever the GA presented such behavior and it is faster than the use of principal components analysis.