Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient computational models with the goal of approximating the fitness function in evolutionary computation systems. This area of research has been active for over two decades and has received significant attention from the specialised research community in different areas, for example, single and many objective optimisation or dynamic and stationary optimisation problems. An emergent and exciting area that has received little attention from the SAEAs community is in neuroevolution. This refers to the use of evolutionary algorithms in the automatic configuration of artificial neural network (ANN) architectures, hyper-parameters and/or the training of ANNs. However, ANNs suffer from two major issues: (a) the use of highly-intense computational power for their correct training, and (b) the highly specialised human expertise required to correctly configure ANNs necessary to get a well-performing network. This work aims to fill this important research gap in SAEAs in neuroevolution by addressing these two issues. We demonstrate how one can use a Kriging Partial Least Squares method that allows efficient computation of good approximate surrogate models compared to the well-known Kriging method, which normally cannot be used in neuroevolution due to the high dimensionality of the data.

翻译：替代模型辅助进化算法（SAEAs）旨在利用高效计算模型逼近进化计算系统中的适应度函数。该研究领域已活跃二十余年，并在不同方向（如单目标与多目标优化、动态与静态优化问题）受到专业研究团体的广泛关注。然而，SAEAs领域鲜有关注的新兴方向是神经演化——即利用进化算法自动配置人工神经网络（ANN）架构、超参数及/或训练ANN。但ANN面临两大难题：(a) 正确训练需要极高计算资源，(b) 配置高性能网络需高度专业化的人工经验。本研究通过解决这两个问题，旨在填补SAEAs在神经演化中的关键研究空白。我们展示了如何运用克里金偏最小二乘法，相较于因数据高维性而无法应用于神经演化的经典克里金方法，该方法能高效构建性能良好的近似替代模型。