Analysis of Evolutionary Diversity Optimization for Permutation Problems
Date
2021
Authors
Do, A.V.
Guo, M.
Neumann, A.
Neumann, F.
Editors
Chicano, F.
Krawiec, K.
Krawiec, K.
Advisors
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Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021), 2021 / Chicano, F., Krawiec, K. (ed./s), pp.574-582
Statement of Responsibility
Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann
Conference Name
Genetic and Evolutionary Computation Conference (GECCO) (10 Jul 2021 - 14 Jul 2021 : Lille, France - Virtual Online)
Abstract
Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks. We contribute to this line of research by studying evolutionary diversity optimization for two of the most prominent permutation problems, namely the Traveling Salesperson Problem (TSP) and Quadratic Assignment Problem (QAP). We explore the worst-case performance of a simple mutation-only evolutionary algorithm with different mutation operators, using an established diversity measure. Theoretical results show most mutation operators for both problems ensure production of maximally diverse populations of sufficiently small size within cubic expected run-time. We perform experiments on QAPLIB instances in unconstrained and constrained settings, and reveal much more optimistic practical performances. Our results should serve as a baseline for future studies.
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