Openly revisiting derivative-free optimization
Jérémy Rapin, Pauline Dorval, Jules Pondard, Nicolas Vasilache, Marie-Liesse Cauwet, Camille Couprie, Olivier Teytaud · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
This paper surveys and compares a wide range of derivative-free optimization algorithms in an open source context. We also propose a genetic variant of differential evolution, an adaptation of population control for the multimodal noise-free case, new multiscale deceptive functions, and as a contribution to the debate on genetic crossovers, a test function with useless variables on which 2-points crossover achieves great performance. We include discrete and continuous and mixed settings; sequential and parallel optimization; rotated, separable and partially rotated settings; noisy and noise-free setting. We include real world applications and compare with recent optimizers which have not yet been extensively compared to the state of the art.