Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning graphical model structure using L1-regularization paths
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Model complexity vs. performance in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Regularized k-order markov models in EDAs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Optimization by ℓ1-constrained Markov fitness modelling
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
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The Bayesian optimization algorithm (BOA) uses Bayesian networks to explore the dependencies between decision variables of an optimization problem in pursuit of both faster speed of convergence and better solution quality. In this paper, a novel method that learns the structure of Bayesian networks for BOA is proposed. The proposed method, called L1BOA, uses L1-regularized regression to find the candidate parents of each variable, which leads to a sparse but nearly optimized network structure. The proposed method improves the efficiency of the structure learning in BOA due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems are carried out, which show that L1BOA outperforms the standard BOA when no a-priori knowledge about the problem structure is available, and nearly achieves the best performance of BOA that applies explicit complexity controls.