Author :
Zeng, Jin ; Ren, Qing-sheng
Author_Institution :
Dept. of Math., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
In this paper, we investigate the usage of history information for estimation of distribution algorithm (EDA). In EDA, the distribution is estimated from a set of selected individuals and then the estimated distribution model is used to generate new individuals. It needs large population size to converge to the global optimum. A new algorithm, the high-order EDA, is proposed based on the idea of filter. By the usage of history information, it can converge to the global optimum with high probability even with small population size. Convergence properties are then discussed. We also show the application for constrained optimization problems.
Keywords :
convergence; genetic algorithms; probability; constrained optimization problem; convergence; estimation-of-distribution algorithm; evolutionary theory; genetic algorithm; global optimum solution; high-order EDA; history information; population-based optimization method; probability; Computer science; Constraint optimization; Cybernetics; Electronic design automation and methodology; Filters; Genetic algorithms; History; Machine learning; Mathematics; Optimization methods; Constraint optimization; Convergence; Estimation of distribution algorithm (EDA); High-order EDA;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212795
