A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9014494_2
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2015.2395073
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 838
- Volume
- 19
- Issue
- 6
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
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-
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- 98
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Completely different from most existing evolutionary multi-objective optimization algorithms where solution diversity is passively taken care of during selection, this work builds an inverse model using Gaussian process for directly sampling new candidate solutions in the objective space. The significance of this paper is that it is not only unique in evolutionary optimisation, but sheds light on solving general inverse modelling problems widely needed in science and engineering. Specifically, it was further developed in [Chen et al, 2016] to solve many-objective problems which could subsequently be applied to important problems in automotive engineering (UoA 11 ICS).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -