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On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization

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Chugh, T., Sindhya, K., Miettinen, K., Hakanen, J., & Jin, Y. (2016). On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization. In J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), Parallel Problem Solving from Nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings (pp. 214-224). Springer International Publishing. Lecture Notes in Computer Science, 9921. https://doi.org/10.1007/978-3-319-45823-6_20
Published in
Lecture Notes in Computer Science
Authors
Chugh, Tinkle |
Sindhya, Karthik |
Miettinen, Kaisa |
Hakanen, Jussi |
Jin, Yaochu
Editors
Handl, Julia |
Hart, Emma |
Lewis, Peter R. |
López-Ibáñez, Manuel |
Ochoa, Gabriela |
Paechter, Ben
Date
2016
Discipline
TietotekniikkaMathematical Information Technology
Copyright
© Springer International Publishing AG. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.

 
Surrogate-assisted evolutionary multiobjective optimization algorithms are often used to solve computationally expensive problems. But their efficacy on handling constrained optimization problems having more than three objectives has not been widely studied. Particularly the issue of how feasible and infeasible solutions are handled in generating a data set for training a surrogate has not received much attention. In this paper, we use a recently proposed Kriging-assisted evolutionary algorithm for many-objective optimization and investigate the effect of infeasible solutions on the performance of the surrogates. We assume that constraint functions are computationally inexpensive and consider different ways of handling feasible and infeasible solutions for training the surrogate and examine them on different benchmark problems. Results on the comparison with a reference vector guided evolutionary algorithm show that it is vital for the success of the surrogate to properly deal with infeasible solutions. ...
Publisher
Springer International Publishing
Parent publication ISBN
978-3-319-45822-9
Conference
International Conference on Parallel Problem Solving From Nature
Is part of publication
Parallel Problem Solving from Nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings
ISSN Search the Publication Forum
0302-9743
Keywords
multiobjective optimization computational cost metamodel evolution control päätöksenteko
DOI
https://doi.org/10.1007/978-3-319-45823-6_20
URI

http://urn.fi/URN:NBN:fi:jyu-201609304240

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/26241627

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