多目标随机规划的交互遗传算法

利用遗传算法在处理过程中不依赖问题的种类，并具有较强鲁棒性等特点，提出了一种基于交互式的求解多目标随机规划的遗传算法，算法的意思是，结合小生境技巧和构造Pareto选优过滤器的手段，通过与决策者的反复交互对话，最后得到使决策者满意的问题的Pateto有效解集。

Interactive Genetic Algorithm for Multiobjective Stochastic Programming

The increasing complexity in decision making process has brought new hard solved problems involving diversity of objectives and various random factors. The generic algorithm (GA) is referred to as an efficient parallel and evolutionary search technique. Because of its independence of problem types in actual models and its better robustness in the iterative process, GA plays an important role in successfully handling complicated multiobjective problems. In this paper, a newly developed stochastic multiobjective genetic algorithm was introduced on basis of interactive approach. Integrated the niche technique with the construction of Pareto set filter, through continuous interaction with decision maker, a new family of Pareto efficient solution which satisfies the decision makers could be obtained.