Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation
Date
2018
Authors
Covantes Osuna, E.
Gao, W.
Neumann, F.
Sudholt, D.
Editors
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Journal Title
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Journal article
Citation
Theoretical Computer Science, 2018; 832:123-142
Statement of Responsibility
Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt
Conference Name
Abstract
Parent selection in evolutionary algorithms for multi-objective optimisation is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points. We propose to refine the parent selection on evolutionary multi-objective optimisation 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 experimental studies that show a correspondence between theory and empirical results and motivate further theoretical investigations in terms of stagnation. We show that stagnation might occur when favouring individuals with a high diversity contribution in the parent selection step and provide a discussion on which scheme to use for more complex problems based on our theoretical and experimental results.
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Dissertation Note
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Available online 19 June 2018
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© 2018 Elsevier B.V. All rights reserved.