DocumentCode :
3138958
Title :
Asynchronous Strategy of Parallel Hybrid Approach of GA and EDA for Function Optimization
Author :
Said, Suhana Mohd ; Nakamura, Mitsutoshi
Author_Institution :
Inf. Eng. Dept., Univ. of the Ryukyus, Nishihara, Japan
fYear :
2012
fDate :
5-7 Dec. 2012
Firstpage :
420
Lastpage :
428
Abstract :
This paper adapts parallel master-slave estimation of distribution and genetic algorithms (GAs and EDAs) hybridization. The master selects portions of the search space, and slaves perform, in parallel and independently, a GA that solves the problem on the assigned portion of the search space. The master´s work is to progressively narrow the areas explored by the slave´s GAs, using parallel dynamic K-means clustering to determine the basins of attraction of the search space. Coordination of activities between master and slaves is done in an asynchronous way (i.e. no waiting is entertained among the processes). The proposed asynchronous model has managed to reduce computation time while maintaining the quality of solutions.
Keywords :
genetic algorithms; pattern clustering; search problems; EDA; GA; asynchronous strategy; function optimization; genetic algorithms; parallel dynamic k-means clustering; parallel hybrid approach; parallel master-slave estimation; search space; Clustering algorithms; Estimation; Genetic algorithms; Heuristic algorithms; Master-slave; Program processors; Vectors; Asynchronous; Estimation of Distribution Algorithm; Genetic Algorithms; Hybrid; K-means clustering; Master-Slave; Parallel processing; Synchronous;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networking and Computing (ICNC), 2012 Third International Conference on
Conference_Location :
Okinawa
Print_ISBN :
978-1-4673-4624-5
Type :
conf
DOI :
10.1109/ICNC.2012.80
Filename :
6424606
Link To Document :
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