Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization

Konstantinos Chatzilygeroudis (), Antoine Cully (), Vassilis Vassiliades () and Jean-Baptiste Mouret ()
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Konstantinos Chatzilygeroudis: Computer Technology Institute & Press “Diophantus” (CTI)
Antoine Cully: Imperial College London
Vassilis Vassiliades: CYENS Centre of Excellence
Jean-Baptiste Mouret: Université de Lorraine, LORIA

A chapter in Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 2021, pp 109-135 from Springer

Abstract: 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.

Date: 2021
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DOI: 10.1007/978-3-030-66515-9_4

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