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Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization

Authors: Chatzilygeroudis, Konstantinos; Cully, Antoine; Vassiliades, Vassilis; Mouret, Jean-Baptiste;

Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization

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.

Comment: 13 pages, 4 figures, 3 algorithms, to be published in "Black Box Optimization, Machine Learning and No-Free Lunch Theorems", P. Pardalos, V. Rasskazova, M.N. Vrahatis, Ed., Springer

Country
United Kingdom
Subjects by Vocabulary

Microsoft Academic Graph classification: Mathematical optimization Computer science Fitness landscape Feature vector Space (commercial competition) Evolutionary computation Local optimum Genotype Reinforcement learning business.industry Deep learning Global optimum Stochastic optimization Artificial intelligence business

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, cs.LG, Machine Learning (stat.ML), Machine Learning (cs.LG), Statistics - Machine Learning, FOS: Mathematics, Neural and Evolutionary Computing (cs.NE), cs.NE, Mathematics - Optimization and Control, math.OC, Computer Science - Neural and Evolutionary Computing, stat.ML, Optimization and Control (math.OC)

35 references, page 1 of 4

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[2] Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. Finitetime analysis of the multiarmed bandit problem. Springer, 2002.

[3] Julio Barrera and Carlos A Coello Coello. A review of particle swarm optimization methods used for multimodal optimization. In Innovations in swarm intelligence, pages 9-37. Springer, 2009.

[5] Hans-Georg Beyer and Hans-Paul Schwefel. Evolution [20] Kalyanmoy Deb and Hans-Georg Beyer. Self-adaptive gestrategies-a comprehensive introduction. Natural comput- netic algorithms with simulated binary crossover. Evoluing, 1(1):3-52, 2002. tionary computation, 9(2):197-221, 2001.

[24] Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune. Go-explore: a new approach for hard-exploration problems. arXiv preprint arXiv:1901.10995, 2019.

[25] Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune. First return then explore. arXiv preprint arXiv:2004.12919, 2020. [OpenAIRE]

[17] Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean- [31] Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. Baptiste Mouret. Robots that can adapt like animals. Nature, Data-efficient design exploration through surrogate-assisted 521(7553):503-507, 2015. illumination. Evolutionary computation, pages 1-30, 2018.

[18] Swagatam Das, Sayan Maity, Bo-Yang Qu, and Ponnuthu- [32] Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. rai Nagaratnam Suganthan. Real-parameter evolutionary Discovering representations for black-box optimization. In multimodal optimization - a survey of the state-of-the-art. Proceedings of the Genetic and Evolutionary Computation Swarm and Evolutionary Computation, 1:71-88, 2011. Conference (GECCO), volume 11, 2020.

[35] Georges R Harik. Finding multimodal solutions using restricted tournament selection. In Proceedings of the 6th International Conference on Genetic Algorithms, pages 24- 31, San Francisco, CA, 1995. Morgan Kaufmann.

[36] Mark Hauschild and Martin Pelikan. An introduction and survey of estimation of distribution algorithms. Swarm and evolutionary computation, 1(3):111-128, 2011.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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