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.

翻译：针对特定问题实例选择最合适的算法仍是一项具有挑战性的任务，尤其是在问题特征随时间演变的在线或动态环境中。仅依赖瞬时性能指标可能导致反应式的不稳定行为，常常引发次优的算法切换。本文提出一种计算高效的方法，用于聚合算法在多个问题实例上的性能表现，该方法能够较好地抵御实例特征的剧烈波动。受强化学习内在特性的启发，该技术将奖励与惩罚封装为隐含收益，进而触发利用与探索机制，最终实现自适应算法切换。所提方法采用遗传算法中的岛屿模型，促进算法种群在本地知识库中的并行探索与性能交换。在排序算法和机器人避障任务上的实验评估验证了该方法的可行性与有效性，凸显了其在自适应算法选择关键领域中的应用潜力。