遗传算法的改进与应用

遗传算法不依赖于具体问题，作为优化方法用于决策支持系统有其明显优势．通常的遗传算法是一种求解非线性无约束优化问题的迭代自适应启发式概率性搜索算法，对于约束优化问题一般采用罚函数法将其化为无约束情形后再运用遗传算法求解．文中提出的基于浮点编码的改进算法，通过构造交叉与变异操作，可用来求解一类约束非线性优化问题．该方法已用于一个决策支持系统，取得了较好的效果

Improvements and Applications of Genetic Algorithm

Genetic algorithm (GA) is a kind of numerical algorithm with properties stochastic, iterative, evolutionary, based on the principles of population genetics and natural selection. One of the appeals of GA is its robust, e.g.,it performs well over a range of problem types, and it requires little knowledge of the problem itself. Therefore, GA is suited to the need of DSS for solving the different optimization problems without numerical optimization background. GA is of essentially unconstrained search procedure. In this paper, an improvement of GA which uses the floating point representation, is discussed. A modified GA by a direct way to incorporate linear constraints is proposed. This approach is used in some optimization problems of a decision support system. The application results show the algorithm is effective.