Title :
Blocked stochastic sampling versus Estimation of Distribution Algorithms
Author :
Santana, Roberto ; Muhlenbein, Heinz
Author_Institution :
ICIMAF, Havana, Cuba
fDate :
6/24/1905 12:00:00 AM
Abstract :
The Boltzmann distribution is a good candidate for a search distribution for optimization problems. We compare two methods to approximate the Boltzmann distribution - Estimation of Distribution Algorithms (EDA) and Markov Chain Monte Carlo methods (MCMC). It turns out that in the space of binary functions even blocked MCMC methods outperform EDA on a small class of problems only. In these cases a temperature of T = 0 performed best
Keywords :
Markov processes; Monte Carlo methods; evolutionary computation; sampling methods; search problems; Boltzmann distribution; Estimation of Distribution Algorithms; Markov Chain Monte Carlo methods; binary functions; blocked stochastic sampling; optimization problems; search distribution; Annealing; Boltzmann distribution; Distributed computing; Electronic design automation and methodology; Partitioning algorithms; Proposals; Sampling methods; Search methods; Stochastic processes; Temperature;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1004446
