Evolution strategies assisted by Gaussian processes with improved preselection criterion

Abstract

Abstract-In many engineering optimization problems, the number of fitness function evaluations is limited by time and cost. These problems pose a special challenge to the field of evolutionary computation, since existing evolutionary methods require a very large number of problem function evaluations. One popular way to address this challenge is the application of approximation models as a surrogate of the real fitness function. We propose a model assisted Evolution Strategy, which uses a Gaussian Process approximation model to pre-select the most promising solutions. To refine the pre-selection process we determine the likelihood of each individual to improve the overall best found solution. Due to this, the new algorithm has a much better convergence behavior and achieves better results than standard evolutionary optimization approaches with less fitness evaluations. Numerical results from extensive simulations on several high dimensional test functions including multimodal functions are presented.

Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem

“…Among these, efficiency is a major challenge. Real-world optimisation problems often involve an immense number of possible solutions, and an EA needs a large number of simulation evaluations before an acceptable solution can be found [3]- [4]. Even with improvements in computer processing speed, one single simulation evaluation may take a couple of minutes to hours or days of computing time [4]- [5].…”

A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems

“…Anna Syberfeldt, Henrik Grimm, Amos Ng, and Robert I. John E therefore be considered in the optimization; otherwise it is very likely that the search would converge to a false optimum [3].…”

“…Among these, efficiency is a major challenge. Real-world optimization problems often involve an immense number of possible solutions, and an EA needs a large number of simulation evaluations before an acceptable solution can be found [2]- [3]. Even with improvements in computer processing speed, one single simulation evaluation may take a couple of minutes to hours or days of computing time [4]- [5].…”

Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)

“…Later, Ulmer et al [30] analyzed GP-based preselection strategy similar to the one of [3] for CMA-ES, where an exploration-based selection criterion was chosen. The results confirmed the observations of [3] that on multimodal functions the exploration-based pre-selection should be preferred to "greedy"f -based pre-selection.…”