Computer Science – Neural and Evolutionary Computing
Scientific paper
2004-06-03
Computer Science
Neural and Evolutionary Computing
Optimization by Building and Using Probabilistic Models OBUPM-2004
Scientific paper
Estimation of Distribution Algorithms have been proposed as a new paradigm for evolutionary optimization. This paper focuses on the parallelization of Estimation of Distribution Algorithms. More specifically, the paper discusses how to predict performance of parallel Mixed Bayesian Optimization Algorithm (MBOA) that is based on parallel construction of Bayesian networks with decision trees. We determine the time complexity of parallel Mixed Bayesian Optimization Algorithm and compare this complexity with experimental results obtained by solving the spin glass optimization problem. The empirical results fit well the theoretical time complexity, so the scalability and efficiency of parallel Mixed Bayesian Optimization Algorithm for unknown instances of spin glass benchmarks can be predicted. Furthermore, we derive the guidelines that can be used to design effective parallel Estimation of Distribution Algorithms with the speedup proportional to the number of variables in the problem.
Ocenasek Jiri
Pelikan Martin
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