A co-evolutionary hybrid algorithm for multi-objective optimization of gene regulatory network models

被引：0

作者：

Koduru, Praveen ^{[1
]}

Das, Sanjoy ^{[1
]}

Welch, Stephen ^{[1
]}

Roe, Judith L. ^{[1
]}

Lopez-Dee, Zenaida P. ^{[1
]}

机构：

[1] Kansas State Univ, Manhattan, KS 66506 USA

来源：

GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2 | 2005年

关键词：

multi-objective; simplex; hybrid; genomics;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the parameters of a genetic network for rice flowering time control have been estimated using a multiobjective genetic algorithm approach. We have modified the recently introduced concept of fuzzy dominance to hybridize the well-known Nelder Mead Simplex algorithm for better exploitation with a multi-objective genetic algorithm. A coevolutionary approach is proposed to adapt the fuzzy dominance parameters. Additional changes to the previous approach have also been incorporated here for faster convergence, including elitism. Our results suggest that this hybrid algorithm performs significantly better than NSGA-11, a standard algorithm for multiobjective optimization.

引用

页码：393 / 399

页数：7

共 17 条

[1]

Coello C. A. C., 1999, Knowledge and Information Systems, V1, P269

[2]

Cooper Mark, 2002, In Silico Biology, V2, P151

[3] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].