Quality and Diversity Optimization: A Unifying Modular Framework

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 alg...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 22; no. 2; pp. 245 - 259
Main Authors: Cully, Antoine, Demiris, Yiannis
Format: Journal Article
Language:English
Published: IEEE 01.04.2018
Subjects:
Algorithm design and analysis
Behavioral diversity
collection of solutions
Evolutionary computation
Legged locomotion
novelty search
Optimization
optimization methods
quality-diversity (QD)
Sociology
Statistics
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary: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 best solution for 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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2704781