Covantes Osuna, E., Gao, W., Neumann, F. et al. (1 more author) (2017) Speeding Up Evolutionary Multi-objective Optimisation Through Diversity-Based Parent Selection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 17). Genetic and Evolutionary Computation Conference (GECCO 2017), 15/07/2017 - 19/07/2017, Berlin. ACM , pp. 553-560.
Abstract
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points, while the reproduction phase involves the application of diversity mechanisms or other methods to achieve a good spread of the population along the Pareto front. We propose to refine the parent selection on evolutionary multi-objective optimization with diversity-based metrics. The aim is to focus on individuals with a high diversity contribution located in poorly explored areas of the search space, so the chances of creating new non-dominated individuals are better than in highly populated areas. We show by means of rigorous runtime analysis that the use of diversity-based parent selection mechanisms in the Simple Evolutionary Multi-objective Optimiser (SEMO) and Global SEMO for the well known bi-objective functions OneMinMax and Lotz can significantly improve their performance. Our theoretical results are accompanied by additional experiments that show a correspondence between theory and empirical results.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 SAGE - 138086 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 May 2017 11:41 |
Last Modified: | 19 Jul 2017 07:17 |
Published Version: | https://doi.org/10.1145/3071178.3080294 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3071178.3080294 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:115996 |