质量多样性优化：随机优化的一个新分支
Quality-Diversity Optimization: a novel branch of stochastic optimization
摘要：
传统的优化算法搜索一个单一的全局最优值，最大化（或最小化）的目标函数。多峰优化算法在搜索空间中搜索可能多于一个的最高峰。质量多样性算法是演化计算工具箱中的一个新成员，它不仅搜索一组局部最优解，而且试图照亮搜索空间。实际上，它们提供了高性能解决方案如何分布在整个搜索空间中的整体视图。与多模态优化算法的主要区别在于：（1）质量多样性通常在行为空间（或特征空间）中工作，而不是在基因型（或参数）空间中工作;（2）质量多样性试图填充整个行为空间，即使小生境不是适应度景观中的峰值。在本章中，我们将对质量多样性优化进行简单的介绍，讨论主要的代表性算法，以及社区中正在考虑的主要当前主题。在本章中，我们还讨论了质量多样性算法的几个成功应用，包括深度学习、机器人和强化学习。
英文摘要：
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning.
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。
