The research of parameters of genetic algorithm and comparison with particle swarm optimization and shuffled frog-leaping algorithm

The paper introduces the principle of genetic algorithm and analyses the selection of parameters of genetic algorithm. By an example, the paper researches the different effect of each parameter. Such as, the size of the population (M), the probability of crossover (P_c) and the probability of mutation (P_m). By the experimentation and simulation, The paper brings forward a general method for selection of parameters for genetic algorithm. In the end, the paper compare the genetic algorithm (GA) with particle swarm optimization (PSO) and shuffled frog-leaping algorithm (SFLA).