A Surrogate Assisted Evolutionary Strategy for Image Approximation by Density-Ratio Estimation

Phoenix Neale Williams, Ke Li, Geyong Min

Abstract: The use of evolutionary strategies in generating images is a common practice within the computational art community. In particular, a popular approach is to approximate a target image using overlapping, semi-transparent shapes and optimizing their attributes to increase similarity to the target image. However, existing methods usually require millions of fitness evaluations to construct good approximations. Within the evolutionary computation and machine learning communities, the use of surrogates has shown to decrease the number of fitness evaluations while achieving state-of-the-art results. Despite the gained traction of surrogate-assisted algorithms, their use within the computational art community is nonexistent. To address this, we extend the previous work of Bayesian Optimization by density-ratio estimation (BORE) to the image approximation task. By estimating the probability of improvement acquisition function using a convolutional probabilistic classifier, we search for solutions that maximize the acquisition function using an evolutionary strategy. By conducting experiments on six different styled target images, we demonstrate the superior performance achieved with the use of surrogate assistance.