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A diversity preserving selection in multiobjective evolutionary algorithms

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Abstract

In this paper, an efficient diversity preserving selection (DPS) technique is presented for multiobjective evolutionary algorithms (MEAs). The main goal is to preserve diversity of nondominated solutions in problems with scaled objectives. This is achieved with the help of a mechanism that preserves certain inferior individuals over successive generations with a view to provide long term advantages. The mechanism selects a group (of individuals) that is statistically furthest from the worst group, instead of just concentrating on the best individuals, as in truncation selection. In a way, DPS judiciously combines the diversity preserving mechanism with conventional truncation selection. Experiments demonstrate that DPS significantly improves diversity of nondominated solutions in badly-scaling problems, while at the same time it exhibits acceptable proximity performance. Whilst DPS has certain advantages when it comes to scaling problems, it empirically shows no disadvantages for the problems with non-scaled objectives.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-dong, Suwon, 440-746, Korea

    Chang Wook Ahn

  2. Department of Information and Communications, Gwangju Institute of Science and Technology, 1 Oryong-dong, Buk-gu, Gwangju, 500-712, Korea

    R. S. Ramakrishna

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  1. Chang Wook Ahn
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  2. R. S. Ramakrishna
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Correspondence to R. S. Ramakrishna.

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Ahn, C.W., Ramakrishna, R.S. A diversity preserving selection in multiobjective evolutionary algorithms. Appl Intell 32, 231–248 (2010). https://doi.org/10.1007/s10489-008-0140-0

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  • DOI: https://doi.org/10.1007/s10489-008-0140-0

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