A Multi-Objective Hybrid Genetic Algorithm for a Flowering Time Gene Network Model in A. thaliana

摘要

Hybrid algorithms that combine genetic algorithms with the Nelder-Mead simplex algorithm have been effective in solving certain optimization problems. In this article, we apply a similar technique to estimate the parameters of a gene regulatory network for flowering time control in Arabidopsis thaliana. The algorithm minimizes the difference between the model behavior and real world data. Because of the nature of the data, a multi-objective approach is necessary. The concept of fuzzy dominance is used for multi-objective optimization. The approach makes use of elitism and the use of an archive to store the best solutions. It uses a hypergrid-based approach to prune the archive size and maintain diversity in the final solution outputs. Results suggest that the proposed method performs well in estimating the model parameters.