用强化学习思想动态切换算法，提升复杂环境下的适应能力
A Reinforcement Learning Inspired Latent Yield Based Adaptive Algorithm Switching Mechanism
- 以强化学习机制构建隐式收益模型，驱动算法选择
- 在排序与避障任务中实现稳定且高效的算法切换
- 适合需要实时自适应的动态系统场景
为动态环境中算法选择难题提出一种计算高效的方法，通过融合多实例性能表现，降低对瞬时特征波动的敏感性。受强化学习启发，将奖励与惩罚整合为隐式收益（latent yield），触发探索与利用机制，实现自适应算法切换。采用类遗传算法的岛屿模型，支持多个算法种群并行演化与性能交换。在排序算法和机器人避障任务上的实验验证了方法的有效性与可行性，展现出在需动态算法选择领域的重要潜力。
原文摘要 · Abstract (English)
Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where problem characteristics evolve over time. Relying solely on instantaneous performance metrics can result in a reactive and unstable behaviour, often leading to suboptimal algorithm switching. This paper introduces a computationally efficient approach for aggregating an algorithm's performance across multiple problem instances that is fairly immune to erratic variations in instance features. Inspired by features inherent to Reinforcement Learning (RL), this technique encapsulates rewards and penalties into a latent yield that, in turn, triggers exploitation and exploration, consequently resulting in adaptive algorithm switching. The proposed technique employs island models, inspired by Genetic Algorithms, to facilitate parallel exploration and performance exchanges among algorithm populations inhabiting local repertoires. Experimental evaluations on sorting algorithms and robotic obstacle avoidance tasks demonstrate the feasibility and effectiveness of the approach, highlighting its potential in domains where adaptive algorithm selection is critical.
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