An effective uniform genetic algorithm for hard optimization problems

Genetic algorithms are one of the effective algorithms for hard optimization problems. They can escape from the local minima, however, the amount of their computation is often large. To decrease the amount of the computation and enhance the algorithms, the uniform design is combined into the genetic algorithm. The new genetic operator has the local-search property similar to that in traditional optimization techniques and needs a minimal amount of computation in certain meaning. Thus the new genetic algorithm can generate a diversity of population and explore the search space effectively. Moreover, the new algorithm is globally convergent. The numerical results also show the effectiveness of the new algorithm with its less computation, higher convergent speed for all test functions.