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：代理辅助进化算法（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。该领域已活跃二十余年，在单目标与多目标优化、动态与静态优化问题等不同方向受到专业研究界的广泛关注。然而，在SAEAs领域中一个新兴且令人兴奋的课题——神经演化——尚未得到充分研究。神经演化是指将进化算法应用于人工神经网络（ANN）架构、超参数的自动配置，以及ANN的训练过程。但人工神经网络面临两大主要挑战：（a）正确训练所需的高强度计算资源，以及（b）配置高性能网络需要高度专业的人类专家经验。本研究旨在填补SAEAs在神经演化领域的重要研究空白，通过解决上述两个问题，展示如何利用克里金偏最小二乘法高效构建良好近似的代理模型——相较于因高维数据限制而通常无法用于神经演化的传统克里金方法，该方法具有显著优势。