Genetic evolution of neural network based on a new three-parents crossover operator

Among the emerging technologies nowadays, the genetic algorithm, a powerful optimization technique, is becoming the subject of new craze among neural network researchers. Genetic algorithms (GAs) for training and designing artificial neural networks (ANNs) have proved to be a useful integration. This paper reports an improvement over earlier work on the genetic evolution of neural network weights using the two-parents multipoint restricted crossover (Double-MRX) operator proposed by Srivastava, Shukla and Srivastava (Microelectronics Journal, vol. 29, no. 11, p.921-31, 1998). In this research, a methodology to improve network convergence is presented by introducing a new concept contrary to natural law, i.e. crossover with randomly selected multiple crossover sites restricted to lie within individual weight boundaries, hence termed as Triple-MRN. In GAs, the search strategy relies more on exchange of information between individual building blocks by exploiting crossover operator. The use of Triple-MRX promotes cooperation among individuals, that better exploits the new genotypic information contained in genome variation. This ensures a much more effective search, both in terms of quality of the solution and speed of convergence as shown by the simulation experiments. Fitness function used in the authors´ study is 1/MSE (mean square error). The effectiveness of the proposed technique is tested by evaluating the capability of neural network to learn a real-world gas identification problem.