The use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from such GAs as they are seen as being highly destructive and detrimental to the performance of the GA. Designing crossover operators that can effectively be applied to NNs has been an active area of research with success limited to specific problem domains. The focus of this study is to use genetic programming (GP) to automatically evolve crossover operators that can be applied to NN weights and used in GAs. A novel GP is proposed and used to evolve both reusable and disposable crossover operators to compare their efficiency. Experiments are conducted to compare the performance of GAs using no crossover operator or a commonly used human designed crossover operator to GAs using GP evolved crossover operators. Results from experiments conducted show that using GP to evolve disposable crossover operators leads to highly effectively crossover operators that significantly improve the results obtained from the GA.

近年来，使用遗传算法 (GA) 演化神经网络 (NN) 权重越来越受欢迎，特别是与梯度下降一起用作突变算子时。然而，此类 GA 中经常省略交叉算子，因为它们被视为具有高度破坏性并且对 GA 的性能有害。设计可有效应用于神经网络的交叉算子一直是一个活跃的研究领域，但其成功仅限于特定的问题领域。本研究的重点是使用遗传编程（GP）自动演化交叉算子，该算子可应用于神经网络权重并在遗传算法中使用。提出了一种新颖的 GP 并用于演化可重用和一次性交叉算子，以比较它们的效率。进行实验来比较不使用交叉算子或常用的人工设计交叉算子的 GA 与使用 GP 进化交叉算子的 GA 的性能。实验结果表明，使用 GP 进化一次性交叉算子可以产生高效的交叉算子，从而显着改善从 GA 获得的结果。