Quality-Diversity Optimization: a novel branch of stochastic optimization

• URL: http://arxiv.org/abs/2012.04322v2
• Date: Thu, 17 Dec 2020 00:50:04 GMT
• Title: Quality-Diversity Optimization: a novel branch of stochastic optimization
• Authors: Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades and Jean-Baptiste Mouret
• Abstract summary: 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.
• Score: 5.677685109155078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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