论文信息 - A genetic algorithm for chance constrained programming

A genetic algorithm for chance constrained programming

Abstract This paper presents a genetic algorithm for chance constrained programming (CCP), including chance constrained goal programming(CCGP), chance constrained multiobjective programming(CCMOP). In order to deal with stochastic constraints, Monte Carlo simulation is employed to check the feasibility of a solution in the proposed genetic algorithm. Finally, we use some numerical examples to illustrate the effectiveness of genetic algorithm for chance constrained programming.

Baoding Liu | Kakuzo Iwamura

[1] A. Charnes,et al. Chance-Constrained Programming , 1959 .

[2] C. Panne,et al. Minimum-Cost Cattle Feed Under Probabilistic Protein Constraints , 1963 .

[3] Reuven Y. Rubinstein,et al. Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.

[4] Yuri Ermoliev,et al. Numerical techniques for stochastic optimization , 1988 .

[5] David B. Fogel,et al. An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[6] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.