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[CS.AI] Towards Self-Evolving Agents: A Human-Inspired Adaptive Exploration-Exploitation Framework

Published at: 2026-07-16 22:00 Last updated: 2026-07-17 08:45
#algorithm #AI #Genetic Network Programming

Abstract

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. Effectively balancing exploration and exploitation is a key aspect of GNP, yet this trade-off has received limited attention in the 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.

Blogger's Review: The introduction of HGNP offers a fresh perspective on Genetic Network Programming by leveraging human development patterns in exploration and exploitation, significantly enhancing agent adaptability and performance. This method’s generalizability and innovation lay a solid foundation for future research and are worthy of attention.

Original Source: https://arxiv.org/abs/2607.11913

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