The Real Number Type Of Genetic Research And Its Applications, Operators Pass

Many problems can be classified into optimization problems, whether from science test or from engineering design. So a lot of researchers have attached importance to optimization computing, and put forward all kinds of algotithms for optimization problems,for instance: simplex algorithm, dynamic programming algorithm, branch-bound algorithm, etc. But these algorithms are all disadvantage and can not be applied to complex nonlinear optimization problems. In reasonable computional time, they can not obtain a approximate satisfied optima for large scale nonlinear problem. However, genetic algorithms is competent for these problems, and is used widely to work out complex nonlinear optimization problems with large scale in practice. Because simple genetic algorithms can not work out effectively the optimiazation problems with multiple dimension and high precision, and make use of the special knowledge of the problems, this paper research genetic algorithms based on float coding. On the base of analysing the shortcoming of genetic algotithms, three improved techniques for genetic algorithms are bring forward in this paper: fuzzy penalty fitness function, random dislocation arithmetic crossover, fuzzy parameter adjust policy, which improve genetic algorithms capability of global convergence and convergent speed. At the same time, the improved genetic algorithms are applied to nonlinear mixed integer problems and complex nonlinear function optimization.