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Gene regulatory network gives the idea about the nature of interaction among the genes present in the DNA of a living species. Detection of gene regulatory network from gene expression data is of prime interest to the researchers. This paper considers modelling of the gene regulatory network identification problem using a fuzzy recurrent neural network, and obtains the interaction weights among the neuron using differential evolution algorithm. A cost function is designed, the minimization of which yields the solution to the problem. In order to improve the solution further, a heuristic based local search is proposed. Computer simulation of the proposed inference algorithm revels that it is able to predict the signs of all the existing weights accurately.