  Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of
complex biological processes. Modeling GRNs is significantly important in order
to reveal fundamental cellular processes, examine gene functions and
understanding their complex relationships. Understanding the interactions
between genes gives rise to develop better method for drug discovery and
diagnosis of the disease since many diseases are characterized by abnormal
behaviour of the genes. In this paper we have reviewed various evolutionary
algorithms-based approach for modeling GRNs and discussed various opportunities
and challenges.
