Title :
The DNA genetic algorithm applied for solving stochastic integer programming expected value models
Author :
Wang, Ming-chun ; Tang, Wan-sheng ; Liu, Xin
Author_Institution :
Syst. Eng. Inst., Tianjin Univ., Tianjin
Abstract :
In this paper, how to use DNA genetic algorithm to solve stochastic integer programming expected value models is discussed. Since DNA genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly. These merits and the technique of stochastic simulation are combined, which for estimating the random variables of stochastic integer programming expected value models problem. Base on them, a best solution of this problem can be found. The classical newspaper-selling boy problem is calculated for testifying the feasibility and effectiveness of this method.
Keywords :
biocomputing; estimation theory; genetic algorithms; integer programming; mathematics computing; random processes; stochastic programming; DNA genetic algorithm; decoding; newspaper-selling boy problem; random variables estimation; stochastic integer programming expected value models; stochastic simulation; Cybernetics; DNA; Decoding; Genetic algorithms; Linear programming; Machine learning; Mathematical programming; Programming profession; Stochastic processes; Stochastic systems; DNA genetic algorithm; Expected value models; Stochastic integer programming; Stochastic simulation;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620554