Neural Network Training Using Genetic Algorithm

This paper presents a MATLAB program that utilizes genetic algorithms to train neural networks, effectively addressing the local optima problem commonly encountered during neural network training. Genetic algorithms simulate natural selection and genetic mechanisms for search optimization, enabling global optimum discovery, while neural networks are computational models mimicking human nervous systems capable of learning and adaptation. The integration of these two algorithms accelerates neural network learning while preventing convergence to suboptimal solutions. The implementation involves key components such as chromosome encoding of network weights, fitness evaluation through mean squared error calculation, and genetic operations including selection, crossover, and mutation. Users can leverage this program to train neural networks more efficiently and achieve superior performance outcomes through population-based evolutionary optimization rather than gradient-based methods.