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

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-II, a standard algorithm for multiobjective optimization. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search – heuristic methods. General Terms Algorithms, Design. Keywords Multi-objective, simplex, hybrid, genomics.

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Praveen Koduru, Sanjoy Das, Stephen Welch, Judith L. Roe, Zenaida P. Lopez-Dee
• Keywords: Fuzzy Dominance, GECCO 2005, Genetic Algorithm, Multiobjective Genetic Algorithm