Grammar-Guided Genetic Programming (GGGP) employs a variety of insights from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we apply principles from the theory of Facilitated Variation and knowledge about heterogeneous mutation rates and mutation effects to improve the variation operators. We term this new method of variation Facilitated Mutation (FM). We test FM performance on the evolution of neural network optimizers for image classification, a relevant task in evolutionary computation, with important implications for the field of machine learning. We compare FM and FM combined with crossover (FMX) against a typical mutation regime to assess the benefits of the approach. We find that FMX in particular provides statistical improvements in key metrics, creating a superior optimizer overall (+0.48\% average test accuracy), improving the average quality of solutions (+50\% average population fitness), and discovering more diverse high-quality behaviors (+400 high-quality solutions discovered per run on average). Additionally, FM and FMX can reduce the number of fitness evaluations in an evolutionary run, reducing computational costs in some scenarios.

翻译：摘要：语法引导遗传编程（GGGP）借鉴进化理论的多种见解，为给定任务自主设计解决方案。进化生物学的最新发现可进一步改进GGGP算法。本文应用易化变异理论原理，结合异质性突变率与突变效应的知识，对变异算子进行优化。我们将这种新型变异方法称为易化突变（FM）。我们在图像分类任务中测试了神经网络优化器进化时的FM性能，该任务作为进化计算中的典型课题，对机器学习领域具有重要影响。通过与典型突变机制对比，我们评估了FM及FM结合交叉算子（FMX）的优势。实验表明，FMX在关键指标上实现统计性提升：生成更优的优化器（平均测试准确率提升0.48%）、提高解的平均质量（种群平均适应度提升50%），并发现更多元化的高质量行为（每轮运行平均发现400个高质量解）。此外，FM与FMX可减少进化过程中的适应度评估次数，从而降低某些场景下的计算成本。