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

Authors:
Praveen Koduru;Sanjoy Das;Stephen Welch;Judith L. Roe;Zenaida P. Lopez-Dee
Affiliations:
Kansas State University, Manhattan, KS;Kansas State University, Manhattan, KS;Kansas State University, Manhattan, KS;Kansas State University, Manhattan, KS;Kansas State University, Manhattan, KS
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 1
Cited 1

An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation

Multi-objective hybrid PSO using µ-fuzzy dominance

Proceedings of the 9th annual conference on Genetic and evolutionary computation

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Abstract

In this paper, the parameters of a genetic network for rice flowering time control have been estimated using a multi-objective 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 co-evolutionary 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-II, a standard algorithm for multi-objective optimization.