Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
An Empirical Investigation of the K2 Metric
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
An empirical analysis of search in GSAT
Journal of Artificial Intelligence Research
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Exploiting a theory of phase transitions in three-satisfiability problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Combinatorial effects of local structures and scoring metrics in bayesian optimization algorithm
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Effective structure learning for EDA via L1-regularizedbayesian networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Graph clustering based model building
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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The Bayesian Optimization Algorithm (BOA) uses a Bayesian network to estimate the probability distribution of promising solutions to a given optimization problem. This distribution is then used to generate new candidate solutions. The objective is to improve the population of candidate solutions by learning and sampling from good solutions. A Bayesian network (BN) is a graphical representation of a probability distribution over a set of variables of a given problem domain. The number of topological states that a BN can create depends on a parameter called maximum allowed indegree. We show that the value of the maximum allowed indegree given to the Bayesian network used by the BOA strongly affects the performance of this algorithm. Furthermore, there is a limited set of values for this parameter for which the performance of the BOA is maximized.