An improved genetic algorithm integrated with a sequential number-theoretic method

For complicated problems, traditional optimization methods cannot obtain the global optimization solution, not even a satisfactory solution, in many situations. Thus stochastic methods have been developed, such as genetic algorithms (GAs). GAs have many advantages, they also have some drawbacks, such as the premature convergence and the low efficiency because of the random search. In this paper, an improved genetic algorithm is proposed in which a sequential number-theoretic method is embedded. The new algorithm has some attractive features, such as the high search speed and the potential of obtaining the global optimization solution. The result of performance analysis of the new algorithm is encouraging