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https://hdl.handle.net/2440/126049
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Type: | Conference paper |
Title: | Speeding up evolutionary multi-objective optimisation through diversity-based parent selection |
Author: | Osuna, E. Neumann, F. Gao, W. Sudholt, D. |
Citation: | GECCO '17: Proceedings of the 2017 Genetic and Evolutionary Computation Conference, 2017 / Bosman, P.A.N. (ed./s), pp.553-560 |
Publisher: | Association for Computing Machinery (ACM) |
Issue Date: | 2017 |
ISBN: | 9781450349208 |
Conference Name: | Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2017 - 19 Jul 2017 : Berlin, Germany) |
Editor: | Bosman, P.A.N. |
Statement of Responsibility: | Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt |
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. |
Keywords: | Parent selection, evolutionary algorithms, multi-objective optimization, diversity mechanisms, runtime analysis, theory |
Rights: | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. |
DOI: | 10.1145/3071178.3080294 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140103400 http://purl.org/au-research/grants/arc/DP160102401 |
Published version: | http://dx.doi.org/10.1145/3071178.3080294 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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