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。该研究领域已活跃二十余年，并在不同方向受到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等。然而，在神经进化这一新兴且令人兴奋的领域中，SAEAs群体的关注度仍然有限。神经进化涉及使用进化算法自动配置人工神经网络（ANN）架构、超参数和/或训练ANN。然而，ANN面临两大难题：（a）其正确训练需要极高的计算资源，（b）正确配置ANN以获得高性能网络需要高度专业化的人类经验。本研究旨在通过解决这两个问题，填补SAEAs在神经进化领域的重要研究空白。我们展示了如何利用克里金偏最小二乘法构建高效的近似代理模型，相较于通常因数据高维性而无法应用于神经进化的经典克里金方法，该方法能够实现高效计算。