Zhou, Aimin and Zhang, Qingfu (2010) A surrogate-assisted evolutionary algorithm for minimax optimization. In: IEEE Congress on Evolutionary Computation (CEC), 18-23 July 2010 , Barcelona .Full text not yet available from this repository.
Minimax optimization requires to minimize the maximum output in all possible scenarios. It is a very challenging problem to evolutionary computation. In this paper, we propose a surrogate-assisted evolutionary algorithm, Minimax SAEA, for tackling minimax optimization problems. In Minimax SAEA, a surrogate model based on Gaussian process is built to approximate the mapping between the decision variables and the objective value. In each generation, most of the new solutions are evaluated based on the surrogate model and only the best one is evaluated by the actual objective function. Minimax SAEA is tested on six benchmark problems and the experimental results show that Minimax SAEA can successfully solve five of them within 110 function evaluations.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
|Divisions:||Faculty of Science and Engineering > Computer Science and Electronic Engineering, School of
Faculty of Science and Engineering > Mathematical Sciences, Department of
|Depositing User:||Jim Jamieson|
|Date Deposited:||07 Mar 2012 16:12|
|Last Modified:||07 Mar 2012 16:12|
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