Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
An efficient cluster and decomposition algorithm for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
Sequential conditional Generalized Iterative Scaling
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Mining maximal hyperclique pattern: A hybrid search strategy
Information Sciences: an International Journal
Maximum-entropy estimated distribution model for classification problems
International Journal of Hybrid Intelligent Systems
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
A clustering algorithm based on an estimated distribution model
International Journal of Business Intelligence and Data Mining
Redundant association rules reduction techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
An efficient compression technique for frequent itemset generation in association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
A note on information entropy measures for vague sets and its applications
Information Sciences: an International Journal
GAKREM: A novel hybrid clustering algorithm
Information Sciences: an International Journal
IEEE Transactions on Multimedia - Special issue on integration of context and content
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
The Pólya information divergence
Information Sciences: an International Journal
Divergence statistics for testing uniform association in cross-classifications
Information Sciences: an International Journal
Mutation Hopfield neural network and its applications
Information Sciences: an International Journal
RFID-based human behavior modeling and anomaly detection for elderly care
Mobile Information Systems
Δ-Entropy: Definition, properties and applications in system identification with quantized data
Information Sciences: an International Journal
RFID-based human behavior modeling and anomaly detection for elderly care
Mobile Information Systems
Information Sciences: an International Journal
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
Information Sciences: an International Journal
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The Estimation of Distribution Algorithm (EDA) model is an optimization procedure through learning and sampling a conditional probabilistic function. The use of conditional density function permits multivariate dependency modelling, which is not captured in a population-based representation, like the classical Genetic Algorithms. The Gaussian model is a simple and widely used model for density estimation. However, an assumption of normality is not realistic for many real-life problems. Alternatively, the maximum-entropy model can be used, which makes no assumption of a normal distribution. One disadvantage of the maximum-entropy model is the learning cost of its parameters. This paper proposes an Adaptive Estimated Maximum-Entropy Distribution (Adaptive MEED) model, which aims to reduce learning complexity of building a model. Adaptive MEED exploits the fact that samples have a low average fitness in the early stage, but they gradually converge to an optima towards the end of the search. Hence, it is not necessary to inference the model with a full account of observed constraints in the early stage of the search. The proposed model attempts to estimate the density function with a dynamic set of samples and active constraints. In addition, the proposed model includes a global sampling function to address the issue of a missing mutation operator. The ergodic convergence properties of the proposed model are discussed with the Markov Chain analysis. The preliminary experimental evaluation shows that the proposed model performs well against genetic algorithms on several clustering problems.