Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Learning Bayesian networks with local structure
Learning in graphical models
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Proceedings of the 5th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation
Sporadic model building for efficiency enhancement of hierarchical BOA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Does overfitting affect performance in estimation of distribution algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Hybrid evolutionary algorithms on minimum vertex cover for random graphs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Empirical analysis of ideal recombination on random decomposable problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
Scalability problems of simple genetic algorithms
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
From mating pool distributions to model overfitting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Performance of evolutionary algorithms on random decomposable problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
Entropy-based substructural local search for the bayesian optimization algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Entropy-based efficiency enhancement techniques for evolutionary algorithms
Information Sciences: an International Journal
Influence of selection on structure learning in markov network EDAs: an empirical study
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Distance-based bias in model-directed optimization of additively decomposable problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Transfer learning, soft distance-based bias, and the hierarchical BOA
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
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The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on four important classes of test problems: concatenated traps, random additively decomposable problems, hierarchical traps and two-dimensional Ising spin glasses with periodic boundary conditions. We argue that although the probabilistic models in hBOA can encode complex probability distributions, analyzing these models is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in consequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.