DocumentCode :
2689837
Title :
Parallel BMDA with probability model migration
Author :
Jaros, Jiri ; Schwarz, Josef
Author_Institution :
Univ. of Technol., Brno
fYear :
2007
fDate :
25-28 Sept. 2007
Firstpage :
1059
Lastpage :
1066
Abstract :
The paper presents a new concept of parallel bivariate marginal distribution algorithm using the stepping stone based model of communication with the unidirectional ring topology. The traditional migration of individuals is compared with a newly proposed technique of probability model migration. The idea of the new xBMDA algorithms is to modify the learning of classical probability model (applied in the sequential BMDA). In the first strategy, the adaptive learning of the resident probability model is used. The evaluation of pair dependency, using Pearson´s chi-square statistics is influenced by the relevant immigrant pair dependency according to the quality of resident and immigrant subpopulation. In the second proposed strategy, the evaluation metric is applied for the diploid mode of the aggregated resident and immigrant subpopulation. Experimental results show that the proposed adaptive BMDA outperforms the traditional concept of individual migration.
Keywords :
genetic algorithms; parallel algorithms; probability; Pearson chi-square statistics; bivariate EDA algorithm; evolutionary algorithm; parallel bivariate marginal distribution algorithm; parallel genetic algorithm; probability model migration; stepping stone based model; unidirectional ring topology; Decision support systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
Type :
conf
DOI :
10.1109/CEC.2007.4424587
Filename :
4424587
Link To Document :
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