Quality and diversity optimization: a unifying modular framework

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Title: Quality and diversity optimization: a unifying modular framework
Author(s): Cully, AHR
Demiris, Y
Item Type: Journal Article
Abstract: 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 optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity 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. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional 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. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity 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 Quality-Diversity algorithms on three different experimental scenarios.
Publication Date: 26-Jun-2017
Date of Acceptance: 11-May-2017
URI: http://hdl.handle.net/10044/1/48539
DOI: https://dx.doi.org/10.1109/TEVC.2017.2704781
ISSN: 1941-0026
Publisher: IEEE
Journal / Book Title: IEEE Transactions on Evolutionary Computation
Copyright Statement: © 2017 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 643783
Keywords: 0801 Artificial Intelligence And Image Processing
0906 Electrical And Electronic Engineering
0806 Information Systems
Publication Status: Published
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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