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，自主为给定任务设计解决方案。进化生物学的最新发现可进一步改进GGGP算法。本文应用促进变异理论以及关于异质性变异率和变异效应的原理，改进变异算子。我们将这种新型变异方法命名为促进变异（FM）。我们测试了FM在图像分类神经网络优化器进化中的性能——这一任务在进化计算领域具有重要价值，并对机器学习领域产生深远影响。我们将FM及FM与交叉组合（FMX）与典型变异机制进行对比，评估该方法的优势。研究发现，FMX在关键指标上具有统计显著性改进：整体优化器性能提升（平均测试准确率提高0.48%），解平均质量提升（平均种群适应度提高50%），并发现更多样化的高质量行为（每次运行平均多发现400个高质量解）。此外，FM和FMX可减少进化过程中的适应度评估次数，在某些场景下降低计算成本。