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
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
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Using previous models to bias structural learning in the hierarchical BOA
Proceedings of the 10th annual conference on Genetic and 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
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Network crossover performance on NK landscapes and deceptive problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Performance of network crossover on NK landscapes and spin glasses
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
Proceedings of the 13th 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
Exploiting prior information in multi-objective route planning
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
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One of the primary advantages of estimation of distribution algorithms (EDAs) over many other stochastic optimization techniques is that they supply us with a roadmap of how they solve a problem. This roadmap consists of a sequence of probabilistic models of candidate solutions of increasing quality. The first model in this sequence would typically encode the uniform distribution over all admissible solutions whereas the last model would encode a distribution that generates at least one global optimum with high probability. It has been argued that exploiting this knowledge should improve EDA performance when solving similar problems. This paper presents an approach to bias the building of Bayesian network models in the hierarchical Bayesian optimization algorithm (hBOA) using information gathered from models generated during previous hBOA runs on similar problems. The approach is evaluated on trap-5 and 2D spin glass problems.