Garrett, A. ; Dozier, G. ; Deb, K. (2007) NEMO: neural enhancement for multiobjective optimization Proceedings of the Congress on Evolutionary Computation (CEC-2007), (Singapore) . pp. 3108-3113.
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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...
Related URL: http://dx.doi.org/10.1109/CEC.2007.4424868
Abstract
In this paper, a neural network approach is presented to expand the Pareto-optimal front for multiobjective optimization problems. The network is trained using results obtained from the nondominated sorting genetic algorithm (NSGA-II) on a set of well-known benchmark multiobjective problems. Its performance is evaluated against NSGA-II, and the neural network is shown to perform extremely well. Using the same number of function evaluations, the neural network produces many times more non-dominated solutions than NSGA-II.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Proceedings of the Congress on Evolutionary Computation (CEC-2007), (Singapore). |
| ID Code: | 81655 |
| Deposited On: | 07 Feb 2012 06:10 |
| Last Modified: | 18 May 2016 23:07 |
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