一篇新的研究论文通过检查基于重组的算子，探讨了笛卡尔遗传编程（CGP）的改进。传统上，CGP主要依赖变异，而重组方法由于感知到的性能限制而常常被忽视。本研究使用符号回归的SRBench平台，研究了两种特定的重组算子：子图交叉和离散表型重组。研究结果表明，通过超参数优化，基于重组的CGP可以获得更高的性能。 AI
影响 这项研究可能为符号回归任务带来更有效的进化算法。
排序理由 该集群包含一篇详细介绍特定人工智能技术研究结果的学术论文。
在 arXiv cs.NE (Neural & Evolutionary) 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.28353v1 Announce Type: cross Abstract: Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years, recombinationbased approaches have long been avoid…
Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years, recombinationbased approaches have long been avoided, due to apparent lack of performance gains. Thi…