Please use this identifier to cite or link to this item: 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: https://doi.org/10.1145/3071178
Appears in Collections:Aurora harvest 8
Computer Science publications

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