Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Bayesian networks with local structure
Learning in graphical models
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Scalability problems of simple genetic algorithms
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Correlation guided model building
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Approximating the search distribution to the selection distribution in EDAs
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Network crossover performance on NK landscapes and deceptive problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Predicting solution rank to improve performance
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
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Using previous models to bias structural learning in the hierarchical boa
Evolutionary Computation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
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This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.