An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

被引：0
|
作者：
Pilat, Martin [1 ]
Neruda, Roman [2 ]
机构：
[1] Charles Univ Prague, Fac Math & Phys, Prague, Czech Republic
[2] Acad Sci Czech Republ, Inst Comp Sci, Prague, Czech Republic
来源：
2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2012年
关键词：
COVARIANCE-MATRIX ADAPTATION; ALGORITHM; SELECTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The paper presents a surrogate-based evolutionary strategy for multiobjective optimization. The evolutionary strategy uses distance based aggregate surrogate models in two ways: as a part of memetic search and as way to pre-select individuals in order to avoid evaluation of bad individuals. The model predicts the distance of individuals to the currently known Pareto set. The newly proposed algorithm is compared to other algorithms which use similar surrogate models on a set of benchmark functions.
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页数：7
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