一种用于同行评审小组组成的遗传算法
A genetic algorithm for peer-review panel composition
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中文总结 AI 辅助
研究科学评审小组组成的约束优化问题，提出用遗传算法，通过基于染色体编码及四个不平衡指标的适应度函数评估，以ESO数据测试，能快速识别低不平衡配置，产生多高质量实现，适用于多种同行评审系统。
中文摘要 AI 辅助
科学评审小组的组成是一个约束优化问题，即在平衡科学专业知识和人口统计学多样性的同时，将有限的专家库分配到多个小组中。随着评审人员数量的增加，可能的小组配置数量迅速增长，穷举搜索在计算上很快变得不切实际。本文提出了一种遗传算法，用于为欧洲南方天文台（ESO）的提案评估过程优化小组组成。通过基于染色体的编码来表示小组分配。使用基于四个不平衡指标（科学专业知识、性别、国家隶属关系和专业资历）的适应度函数来评估候选解决方案。该方法使用ESO提案处理系统的真实评审人员数据进行测试。结果表明，遗传算法能快速识别出不平衡程度远低于随机分配的小组配置，并逐步提高总体质量。该方法不仅能产生单一优化配置，还能生成一组高质量的小组实现，可根据适应度函数中未明确包含的其他操作约束进行筛选。虽然该方法是为ESO开发的，但具有通用性，适用于广泛的基于小组的同行评审系统。
英文摘要
The composition of scientific review panels is a constrained optimization problem in which a finite pool of experts must be distributed among multiple panels while balancing scientific expertise and demographic diversity. As the number of possible panel configurations grows very rapidly with the number of reviewers, exhaustive searches rapidly become computationally impractical. In this paper I present a genetic algorithm designed to optimize panel composition for the European Southern Observatory (ESO) proposal evaluation process. Panel assignments are represented through a chromosome-based encoding. Candidate solutions are evaluated using a fitness function based on four imbalance indicators: scientific expertise, gender, country affiliation, and professional seniority. The method is tested using real reviewer data from the ESO proposal handling system. The results show that the genetic algorithm rapidly identifies panel configurations with substantially lower imbalance than those obtained from random assignments and progressively improves the quality of the overall population. Beyond producing a single optimized configuration, the approach generates a set of high-quality panel realizations that can subsequently be filtered according to additional operational constraints not explicitly included in the fitness function. Although developed for ESO, the methodology is general and applicable to a wide range of panel-based peer-review systems.
