An interactive genetic algorithm for multiobjective stochastic programming

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