DocumentCode :
445572
Title :
Empirical studies on parallel network construction of Bayesian optimization algorithms
Author :
Munetomo, Masaharu ; Murao, Naoya ; Akama, Kiyoshi
Author_Institution :
Inf. Initiative Center, Hokkaido Univ., Sapporo, Japan
Volume :
2
fYear :
2005
fDate :
2-5 Sept. 2005
Firstpage :
1524
Abstract :
This paper discusses a parallel optimization algorithm based on evolutionary algorithms with probabilistic model-building in order to design a robust search algorithm that can be applicable to a wide-spectrum of application problems effectively and reliably. Probabilistic model building genetic algorithm, which is also called estimation of distribution algorithm, is a promising approach in evolutionary computation and its parallelization has been investigated. We propose an improvement of parallel network construction in distributed Bayesian optimization algorithms which estimate distribution of promising solutions as Bayesian networks. Through numerical experiments on an actual parallel architecture, we show the effectiveness of our approach compared to the conventional parallelization. Also we perform experiments on a real-world application problem: protein structure predictions.
Keywords :
belief networks; genetic algorithms; parallel algorithms; search problems; statistical distributions; Bayesian networks; distributed Bayesian optimization algorithm; estimation of distribution algorithm; evolutionary algorithms; genetic algorithm; parallel architecture; parallel network; probabilistic model; protein structure prediction; search algorithm; Algorithm design and analysis; Bayesian methods; Design optimization; Electronic design automation and methodology; Encoding; Evolutionary computation; Genetic algorithms; Parallel architectures; Proteins; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
Type :
conf
DOI :
10.1109/CEC.2005.1554870
Filename :
1554870
Link To Document :
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