Can the Problem-Solving Benefits of Quality Diversity Be Obtained without Explicit Diversity Maintenance?

Can the Problem-Solving Benefits of Quality Diversity Be Obtained without Explicit Diversity Maintenance?
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在没有明确的多样性维护的情况下,能否获得质量多样性解决问题的好处?

DOI:
10.1145/3583133.3596336
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发表时间:
2023
期刊:
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
影响因子:
--
通讯作者:
Spector, Lee
Spector, Lee
中科院分区:
--
文献类型:
--
作者:
Boldi, Ryan;Spector, Lee

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当使用质量多样性(QD)优化来解决困难的探索或欺骗性搜索问题时,我们假设多样性是非常有价值的。这意味着多样性对帮助我们实现目标很重要,但它本身并不是一个目标。通常,在这些领域中,从业者将他们的QD算法与单目标优化框架进行基准测试。在本文中,我们认为,正确的比较,应作出多目标优化框架。这是因为单目标优化框架依赖于子目标的聚合,这可能导致减少对自动维护不同群体至关重要的信息。为了便于质量多样性和多目标优化之间的公平比较,我们提出了一种方法,利用降维自动确定一组行为描述符的个人,以及一组目标的个人解决。使用前者,可以使用标准质量多样性优化技术生成解决方案,并且使用后者,可以使用标准多目标优化技术生成解决方案。这允许在这两类算法之间进行水平比较,而不需要特定于域和算法的修改来促进比较。
When using Quality Diversity (QD) optimization to solve hard exploration or deceptive search problems, we assume that diversity is extrinsically valuable. This means that diversity is important to help us reach an objective, but is not an objective in itself. Often, in these domains, practitioners benchmark their QD algorithms against single objective optimization frameworks. In this paper, we argue that the correct comparison should be made tomulti-objectiveoptimization frameworks. This is because single objective optimization frameworks rely on the aggregation of sub-objectives, which could result in decreased information that is crucial for maintaining diverse populations automatically. In order to facilitate a fair comparison between quality diversity and multi-objective optimization, we present a method that utilizes dimensionality reduction to automatically determine a set of behavioral descriptors for an individual, as well as a set of objectives for an individual to solve. Using the former, one can generate solutions using standard quality diversity optimization techniques, and using the latter, one can generate solutions using standard multi-objective optimization techniques. This allows for a level comparison between these two classes of algorithms, without requiring domain and algorithm specific modifications to facilitate a comparison.
通过质量多样性搜索学习高度多样化的机器人投掷动作
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