Generative Evolutionary Strategy For Black-Box Optimizations

Changhwi Park, Seong Ryeol Kim, Young-Gu Kim, Dae Sin Kim

Keywords: Optimization, Blackbox, Generative model, Evolution

TL;DR: We designed a black-box optimizer for high dimensional search space. It is a cooperative strategy of evolution and generative neural network.

Abstract: Many scientific and technological problems are related to optimization. Among them, black-box optimization in high-dimensional space is particularly challenging. Recent neural network-based black-box optimization studies have shown noteworthy achievements. However, their capability in high-dimensional search space is still limited. This study proposes a black-box optimization method based on evolution strategy and generative neural network model. We designed the algorithm so that the evolutionary strategy and the generative neural network model work cooperatively with each other. This hybrid model enables reliable training of surrogate networks; it optimizes multi-objective, high-dimensional, and stochastic black-box functions. In this experiment, our method outperforms baseline optimization methods, including , including evolution strategies, and a Bayesian optimization.