A Hybrid Genetic Algorithm for Function Optimization
一种函数优化问题的混合遗传算法

To overcome the problem of slow convergence before the genetic algorithms (GAs) reach the global optima, it is an effective way to combine the conventional local search algorithms with GAs. A new hybrid algorithm that incorporates the flexible polyhedron method into the orthogonal genetic algorithm (OGA) is presented in this paper. The experiments showed that it can achieve better performance by performing global search and local search alternately. The new algorithm can be applied to solve the function optimization problems efficiently.