Handling uncertainty in evolutionary multiobjective optimization: SPGA

被引：27

作者：

Eskandari, Hamidreza ^{[1
]}

Geiger, Christopher D. ^{[2
]}

Bird, Robert ^{[1
,3
]}

机构：

[1] Red Lambda Inc, 2180 W State Rd 434, Longwood, FL 32779 USA

[2] Univ Ctr Orlando, Dept Ind & Management Syst Engn, Orlando, FL 2816 USA

[3] Red Lambda Inc, Longwood 32779, FL USA

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4425010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an extension of the previously developed approach to solve multiobjective optimization problems in deterministic environments by incorporating a stochastic Pareto-based solution ranking procedure. The proposed approach, called stochastic Pareto genetic algorithm (SPGA), employs some statistical analysis on the solution dominance in stochastic problem environments to better discriminate among the competing solutions. Preliminary computational results on three published test problems for different levels of noise with SPGA and NSGA-II are discussed.

引用

页码：4130 / +

页数：2

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