Parallel Mixed Bayesian Optimization Algorithm: A Scaleup Analysis

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dc.creatorOcenasek, Jiri
dc.creatorPelikan, Martin
dc.date2004-06-03
dc.date.accessioned2026-07-07T03:21:24Z
dc.date.available2026-07-07T03:21:24Z
dc.descriptionEstimation 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.
dc.descriptionOptimization by Building and Using Probabilistic Models OBUPM-2004
dc.identifierhttps://arxiv.org/abs/cs/0406007
dc.identifierhttp://arxiv.org/abs/cs/0406007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32183
dc.subjectNeural and Evolutionary Computing
dc.subjectDistributed, Parallel, and Cluster Computing
dc.subjectG.1.6; G.3; I.2.6; I.2.8
dc.titleParallel Mixed Bayesian Optimization Algorithm: A Scaleup Analysis
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