Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems
In this paper, we present a novel cross-surrogate assisted memetic algorithm (CSAMA) as a manifestation of multi co-objective evolutionary computation to enhance the search on computationally expensive problems by means of transferring, sharing and reusing information across objectives. In particula...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/102007 http://hdl.handle.net/10220/12022 |
| _version_ | 1826118065513824256 |
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| author | Le, Minh Nghia Ong, Yew Soon Menzel, Stefan Seah, Chun-Wei Sendhoff, Bernhard |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Le, Minh Nghia Ong, Yew Soon Menzel, Stefan Seah, Chun-Wei Sendhoff, Bernhard |
| author_sort | Le, Minh Nghia |
| collection | NTU |
| description | In this paper, we present a novel cross-surrogate assisted memetic algorithm (CSAMA) as a manifestation of multi co-objective evolutionary computation to enhance the search on computationally expensive problems by means of transferring, sharing and reusing information across objectives. In particular, the construction of surrogate for one objective is augmented with information from other related objectives to improve the prediction quality. The process is termed as a cross-surrogate modelling methodology, which will be used in lieu with the original expensive functions during the evolutionary search. Analyses on the prediction quality of the cross-surrogate modelling and the search performance of the proposed algorithm are conducted on the benchmark problems with assessments made against several state-of-the-art multiobjective evolutionary algorithms. The results obtained highlight the efficacy of the proposed CSAMA in attaining high quality Pareto optimal solutions under limited computational budget. |
| first_indexed | 2024-10-01T04:37:38Z |
| format | Conference Paper |
| id | ntu-10356/102007 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:37:38Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1020072020-05-28T07:18:15Z Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems Le, Minh Nghia Ong, Yew Soon Menzel, Stefan Seah, Chun-Wei Sendhoff, Bernhard School of Computer Engineering IEEE Congress on Evolutionary Computation (2012 : Brisbane, Australia) DRNTU::Engineering::Computer science and engineering In this paper, we present a novel cross-surrogate assisted memetic algorithm (CSAMA) as a manifestation of multi co-objective evolutionary computation to enhance the search on computationally expensive problems by means of transferring, sharing and reusing information across objectives. In particular, the construction of surrogate for one objective is augmented with information from other related objectives to improve the prediction quality. The process is termed as a cross-surrogate modelling methodology, which will be used in lieu with the original expensive functions during the evolutionary search. Analyses on the prediction quality of the cross-surrogate modelling and the search performance of the proposed algorithm are conducted on the benchmark problems with assessments made against several state-of-the-art multiobjective evolutionary algorithms. The results obtained highlight the efficacy of the proposed CSAMA in attaining high quality Pareto optimal solutions under limited computational budget. 2013-07-23T02:56:00Z 2019-12-06T20:48:16Z 2013-07-23T02:56:00Z 2019-12-06T20:48:16Z 2012 2012 Conference Paper Le, M. N., Ong, Y. S., Menzel, S., Seah, C.-W., & Sendhoff, B. (2012). Multi co-objective evolutionary optimization: Cross surrogate augmentation for computationally expensive problems. 2012 IEEE Congress on Evolutionary Computation (CEC). https://hdl.handle.net/10356/102007 http://hdl.handle.net/10220/12022 10.1109/CEC.2012.6252915 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Le, Minh Nghia Ong, Yew Soon Menzel, Stefan Seah, Chun-Wei Sendhoff, Bernhard Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems |
| title | Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems |
| title_full | Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems |
| title_fullStr | Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems |
| title_full_unstemmed | Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems |
| title_short | Multi co-objective evolutionary optimization : cross surrogate augmentation for computationally expensive problems |
| title_sort | multi co objective evolutionary optimization cross surrogate augmentation for computationally expensive problems |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/102007 http://hdl.handle.net/10220/12022 |
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