发表机构
Fasa University; Deakin University; Chulalongkorn University; Torrens University Australia; Obuda University（法萨大学; 迪肯大学; 朱拉隆功大学; 澳大利亚托伦斯大学; 奥布达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究受人类发育模式启发，提出人类启发的遗传网络编程（HGNP）框架，通过新型自适应交叉、变异算子及循环消除机制，动态调节遗传网络编程中探索与利用的平衡，提升了智能体策略性能，且可应用于多种GNP变体。
AI 中文摘要
近期基于智能体的人工智能发展趋向于基于图的方法，遗传网络编程（GNP）就是利用有向图为智能体进化可解释决策结构的自进化算法。但GNP中探索与利用的平衡在文献中关注有限。本文受人类发育模式启发，将GNP判断节点转换映射为思考，处理节点映射为行动，提出人类启发的GNP（HGNP）。该方法包含新型自适应交叉和变异算子及循环消除机制，不仅改善进化过程，还能依目标环境和搜索空间特征调整探索-利用平衡，比标准GNP通过交叉和变异概率调整更有效，且可应用于几乎所有GNP变体。在Tileworld基准测试中，HGNP与标准GNP及两种新变体结合评估，显著提升了智能体策略性能，HGNP与基于情境的GNP结合效果最佳。
英文摘要
Recent advancements in agentic AI have increasingly moved toward graph-based methods, driven by the demand for explainable, human-centered, and non-linear reasoning workflows. A prominent example is Genetic Network Programming (GNP), a self-evolving algorithm that utilizes directed graphs to evolve interpretable decision structures for agents. As in most evolutionary algorithms, effectively balancing exploration and exploitation is a key aspect of GNP. However, this trade-off has received limited attention in the GNP literature. To address this gap, we draw inspiration from human developmental patterns, where children prioritize broad experimentation and action over deliberation, with this tendency reversing with age. By mapping transitions between GNP's judgment nodes to deliberation and processing nodes to action, we propose Human-Inspired GNP (HGNP), a novel adaptive framework that dynamically regulates the exploration-exploitation balance throughout the evolutionary process. The method consists of novel adaptive crossover and mutation operators, and a cycle elimination mechanism. HGNP not only improves the evolutionary process but also provides a framework for adjusting the exploration-exploitation balance based on the characteristics of the target environment and its search space. This approach is more effective than tuning via crossover and mutation probabilities in standard GNP. The modifications are general and can be applied to almost all GNP variants. When integrated with standard GNP and two recently introduced GNP variants and evaluated on the Tileworld benchmark, HGNP demonstrated significant performance improvement in agents' strategy. The combination of HGNP with Situation-based GNP (HGNP-SBGNP) achieved the best overall results.
