Messy genetic algorithm for evolving mathematical function evaluating variable length gene regulatory networks

Evolutionary algorithms (EAs) have been successfully used in many studies for evolving both the structure and parameters of biological networks including gene regulatory networks that demonstrate different functionalities. However, most of these studies have used only mutation as the genetic operator in the evolutionary framework, perhaps due to the difficulty of implementing the crossover operation that generates the feasible network models. Nevertheless, crossover is considered to be the most powerful operator of EA which preserves the building blocks and promote quick convergence to a global optima. In this work we propose to use a Messy Genetic Algorithm (MGA) for evolving biological reaction networks that can calculate mathematical functions. The tactful encoding of MGA for reaction networks using a variable length chromosome, allows the use of crossover as well as mutation for the problem in hand that results in a fully functional EA. Earlier MGA has been used for solving many complex problems for which solution encoding is difficult. We used the proposed MGA for evolving different types of mathematical function calculating networks and the success was very encouraging. The evolved networks were able to calculate the target functions for mutually exclusive test data sets satisfactorily. Comparing with some other existing method based on Asexual Evolution (AE), the proposed method was superior in terms of different functions it could successfully evolve and the accuracy at which it could calculate those functions