解决进化算法中的探索-利用困境：一种新型以人为中心的框架
Resolving the Exploration-Exploitation Dilemma in Evolutionary Algorithms: A Novel Human-Centered Framework
AI总结：
针对进化算法中的探索-利用困境，提出以人为中心的两阶段搜索框架HCTPS，通过外部参数SSCP实现独立协调控制与自适应调节，在保障收敛机制的同时提升了搜索空间覆盖率，且设计上算法无关。
摘要：
进化算法（Evolutionary Algorithms, EAs）是用于复杂搜索和优化任务的广泛采用工具；然而，由于缺乏一个允许系统调节探索-利用权衡（这对高效收敛至关重要）的总体操作框架，限制了其潜力的充分发挥，从而导致了算法设计中所谓的探索-利用困境。要系统解决这一困境，需要：（1）对探索和利用进行独立且协调的控制，以及（2）一种明确、计算可行的自适应调节机制。当前几乎分散的、以传统参数调节为中心的方法——在编码施加的结构约束下，缺乏满足这些要求的基础。我们提出了一种以人为中心的两阶段搜索（Human-Centered Two-Phase Search, HCTPS）框架，通过一个外部配置变量——搜索空间控制参数（Search Space Control Parameter, SSCP）来实现（1）和（2）的落地。作为HCTPS的唯一控制旋钮，SSCP集中了探索调整，使用户免于微管理具有难以理解相互依赖关系的传统参数。在这种结构中，人类用户充当元参数，通过调整SSCP自适应地引导调节过程。我们证明，HCTPS在搜索空间覆盖率方面严格超越了当前方法，且不破坏EAs固有的收敛机制；我们以遗传算法作为底层启发式方法，在一组全局基准无约束优化问题上展示了其具体实例，对所提框架进行了全面评估，并展望了未来的研究方向。任何容易出现此困境的搜索算法都可以结合所提框架应用，因为其在设计上是算法无关的。
英文摘要：
Evolutionary Algorithms (EAs) are widely employed tools for complex search and optimization tasks; however, the absence of an overarching operational framework that permits a systematic regulation of the exploration-exploitation tradeoff--critical for efficient convergence--restricts the full actualization of their potential, leading to the so-called exploration-exploitation dilemma in algorithm design. A systematic resolution to this dilemma requires: (1) an independent yet coordinated control over exploration and exploitation, and (2) an explicit, computationally feasible, adaptive regulation mechanism. The current, almost decentralized, traditional parameter tuning-centeric approach--lacks the foundation to satisfy these requirements under encoding-imposed structural constraints. We propose a Human-Centered Two-Phase Search (HCTPS) framework, in which the actualization of (1) and (2) is enabled through an external configuration variable--the Search Space Control Parameter (SSCP). As the sole control knob of HCTPS, the SSCP centralizes exploration adjustments, sparing users from micromanaging traditional parameters with unintelligible interdependencies. To this construct, the human user serves as a meta-parameter, adaptively steering the regulatory process via SSCP adjustments. We prove that the HCTPS strictly surpasses the current approach in terms of search space coverage without disrupting the EAs' inherent convergence mechanisms, demonstrate a concrete instantiation of it--using the Genetic Algorithm as the underlying heuristic on a suite of global benchmark unconstrained optimization problems, provide a through assessment of the proposed framework, and envision future research directions. Any search algorithm prone to this dilemma can be applied in light of the proposed framework, being algorithm-agnostic by design.
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