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：代理辅助进化算法旨在通过高效计算模型近似进化计算系统中的适应度函数。该研究方向已活跃二十余年，并受到不同领域专业研究群体的广泛关注，例如单目标与多目标优化、动态与静态优化问题。然而，在神经进化领域，代理辅助进化算法研究群体关注甚少，这正是一个新兴且引人注目的方向。神经进化指利用进化算法自动配置人工神经网络架构、超参数及/或训练人工神经网络。但人工神经网络面临两个主要挑战：(a) 正确训练需要消耗高强度的计算资源，(b) 正确配置人工神经网络以获得高性能网络需要高度专业化的领域知识。本研究旨在通过解决这两个问题，填补代理辅助进化算法在神经进化领域的重要研究空白。我们展示了如何利用克立金偏最小二乘法构建高效且近似性良好的代理模型——相较于因数据高维性而通常无法应用于神经进化的经典克立金方法，该方法具有显著优势。