Evolutionary optimization using graphical models
New Generation Computing - Special issue on real world computing project
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
Optimal Mutation Rate Using Bayesian Priors for Estimation of Distribution Algorithms
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation
Evaluation relaxation using substructural information and linear estimation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Addressing sampling errors and diversity loss in UMDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
iBOA: the incremental bayesian optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An application of a multivariate estimation of distribution algorithm to cancer chemotherapy
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
Estimation of distribution algorithms with mutation
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
A Markovianity based optimisation algorithm
Genetic Programming and Evolvable Machines
Influence of selection on structure learning in markov network EDAs: an empirical study
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Structure learning is a crucial component of a multivariate Estimation of Distribution algorithm. It is the part which determines the interactions between variables in the probabilistic model, based on analysis of the fitness function or a population. In this paper we take three different approaches to structure learning in an EDA based on Markov networks and use measures from the information retrieval community (precision, recall and the F-measure) to assess the quality of the structures learned. We then observe the impact that structure has on the fitness modelling and optimisation capabilities of the resulting model, concluding that these results should be relevant to research in both structure learning and fitness modelling.