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A surrogate-assisted evolutionary algorithm for minimax optimization

Zhou, A and Zhang, Q (2010) A surrogate-assisted evolutionary algorithm for minimax optimization. In: UNSPECIFIED, ? - ?.

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Abstract

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. © 2010 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 07 Mar 2012 16:12
Last Modified: 17 Oct 2019 21:15
URI: http://repository.essex.ac.uk/id/eprint/1993

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