Quality and Diversity Optimization: A Unifying Modular Framework
Quality and Diversity Optimization: A Unifying Modular Framework
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DOI：
10.1109/tevc.2017.2704781
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发表时间：
2018-04-01
影响因子：
14.3
通讯作者：
Demiris, Yiannis
中科院分区：
文献类型：
--
作者：
Cully, Antoine;Demiris, Yiannis
根据一个或多个目标对功能进行优化以找到最佳解决方案在许多工程和研究领域中具有核心作用。最近，引入了一系列新的优化算法，称为质量多样性（QD）优化，并与经典算法进行对比。 QD 算法不是搜索单一解决方案，而是搜索大量多样化且高性能的解决方案。该集合的作用是尽可能地覆盖可能的解决方案类型的范围，并包含每种类型的最佳解决方案。本文的贡献有三个方面。首先，我们提出了一个统一的 QD 优化算法框架，涵盖该家族的两个主要算法（表型精英的多维档案和局部竞争的新颖性搜索），并强调了该家族中可以研究的多种变体。其次，我们提出了具有新的 QD 算法选择机制的算法，该机制优于本文中测试的所有算法。最后，我们提出了一种新的集合管理，可以克服使用非结构化集合时观察到的侵蚀问题。这三项贡献得到了 QD 算法在三种不同实验场景下的广泛实验比较的支持。
The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named quality-diversity (QD) optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, QD algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the hest solution fir each type. The contribution of this paper is threefold. First, we present a unifying framework of QD optimization algorithms that covers the two main algorithms of this family (multidimensional archive of phenotypic elites and the novelty search with local competition), and that highlights the large variety of variants that can be investigated within this family. Second, we propose algorithms with a new selection mechanism for QD algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of QD algorithms on three different experimental scenarios.