Parameter determination of an evolving neural network approach in unit commitment solution

In this paper we will utilize the GA algorithm to evolve the weight and the interconnection of the neural network to solve the unit commitment problem. We will emphasize on the determination of the appropriate GA parameters to evolve the neural network, i.e. the population size and probabilities of crossover and mutation, and the method used for selection amongst generations such as tournament selection, roulette wheel selection and ranking selection. Performance comparisons are conducted to analyze the learning curve of different parameters, to find out which has a dominant influence on the effectiveness of the algorithm