Monte Carlo Elites：质量多样性选择作为多臂Bandit问题
Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem
摘要：
进化搜索的一个核心挑战是需要在搜索空间的探索和高度拟合区域的利用之间取得平衡。质量多样性搜索明确地在人口的多样性和质量之间走钢丝。本文扩展了一个流行的质量多样性搜索算法，MAP-Elites，通过处理选择父母作为一个多臂强盗问题。使用置信度上限的变化来从搜索空间的未充分探索但潜在有益的区域中选择父区域，可以加速新区域的发现，并提高其存档的总体质量。这篇论文测试了一个间接的亲本选择质量的衡量标准：亲本后代的存活率。结果表明，在探索和开发之间保持平衡，可以在三个不同的测试平台中获得最多样化和高质量的解决方案。
英文摘要：
A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicitly walked this tightrope between a population's diversity and its quality. This paper extends a popular quality-diversity search algorithm, MAP-Elites, by treating the selection of parents as a multi-armed bandit problem. Using variations of the upper-confidence bound to select parents from under-explored but potentially rewarding areas of the search space can accelerate the discovery of new regions as well as improve its archive's total quality. The paper tests an indirect measure of quality for parent selection: the survival rate of a parent's offspring. Results show that maintaining a balance between exploration and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
