Elements of information theory
Elements of information theory
Learning in graphical models
Evolutionary algorithms: from recombination to search distributions
Theoretical aspects of evolutionary computing
Graphical Belief Modeling
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Nonserial Dynamic Programming
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
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
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximate factorizations of distributions and the minimum relative entropy principle
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Self-Organized Modularization in Evolutionary Algorithms
Evolutionary Computation
Side chain placement using estimation of distribution algorithms
Artificial Intelligence in Medicine
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Cross entropy and adaptive variance scaling in continuous EDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Extended probe method for linkage discovery over high-cardinality alphabets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
On the effectiveness of distributions estimated by probabilistic model building
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Network Analysis of Genetic Algorithms
IEICE - Transactions on Information and Systems
On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Why one must use reweighting in estimation of distribution algorithms
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
On multivariate genetic systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Multivariate multi-model approach for globally multimodal problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Sensibility of linkage information and effectiveness of estimated distributions
Evolutionary Computation
On properties of genetic operators from a network analytical viewpoint
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Hierarchical allelic pairwise independent functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Comparison-based complexity of multiobjective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Higher-order linkage learning in the ECGA
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Using symmetry and evolutionary search to minimize sorting networks
The Journal of Machine Learning Research
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Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper we explain the relationship of EDA to algorithms developed in statistics, artificial intelligence, and statistical physics. The major design issues are discussed within a general interdisciplinary framework. It is shown that maximum entropy approximations play a crucial role. All proposed algorithms try to minimize the Kullback-Leibler divergence KLD between the unknown distribution p(x) and a class q(x) of approximations. However, the Kullback-Leibler divergence is not symmetric. Approximations which suppose that the function to be optimized is additively decomposed (ADF) minimize KLD(q||p), the methods which learn the approximate model from data minimize KLD(p||q). This minimization is identical to maximizing the log-likelihood. In the paper three classes of algorithms are discussed. FDA uses the ADF to compute an approximate factorization of the unknown distribution. The factors are marginal distributions, whose values are computed from samples. The second class is represented by the Bethe-Kikuchi approach which has recently been rediscovered in statistical physics. Here the values of the marginals are computed from a difficult constrained minimization problem. The third class learns the factorization from the data. We analyze our learning algorithm LFDA in detail. It is shown that learning is faced with two problems: first, to detect the important dependencies be- tween the variables, and second, to create an acyclic Bayesian network of bounded clique size.