通过节点保留机制提升遗传编程交叉效率，改善搜索性能。
Node Preservation and its Effect on Crossover in Cartesian Genetic Programming
- 引入节点保留策略，防止交叉破坏指令结构。
- 在符号回归任务中，新方法比传统单突变策略更快收敛。
- 适合需要高效进化搜索的自动化程序生成场景。
尽管交叉在其他遗传编程形式（如线性与树形）中至关重要，但普遍认为其会降低笛卡尔遗传编程（CGP）的搜索性能，因此当前主流采用仅使用突变的(1+λ)进化策略。尽管已有若干改进算子表现优于传统方法，但尚未形成通用解决方案。本文比较了基础的一点交叉与均匀交叉，以及节点保留型变体（如罗马·卡尔克雷特提出的子图交叉），其核心在于交叉时不允许拆分指令节点。同时对比了节点突变（整体替换节点）与传统点突变。实验表明，无论在突变还是交叉中引入节点保留，均能显著提升符号回归基准任务的搜索效率，推动了CGP交叉问题的通用解法进展。
原文摘要 · Abstract (English)
While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone $(1+λ)$ evolutionary strategy has become the canonical approach for CGP. Although several operators have been developed that demonstrate an increased performance over the canonical method, a general solution to the problem is still lacking. In this paper, we compare basic crossover methods, namely one-point and uniform, to variants in which nodes are ``preserved,'' including the subgraph crossover developed by Roman Kalkreuth, the difference being that when ``node preservation'' is active, crossover is not allowed to break apart instructions. We also compare a node mutation operator to the traditional point mutation; the former simply replaces an entire node with a new one. We find that node preservation in both mutation and crossover improves search using symbolic regression benchmark problems, moving the field towards a general solution to CGP crossover.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。