Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Elements of information theory
Elements of information theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Genetic Algorithms, Clustering, and the Breaking of Symmetry
PPSN VI Proceedings of the 6th 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
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Effective linkage learning using low-order statistics and clustering
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Sensibility of linkage information and effectiveness of estimated distributions
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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This paper proposes an estimation of distribution algorithm (EDA) aiming at addressing globally multimodal problems, i.e., problems that present several global optima. It can be recognized that many real-world problems are of this nature, and this property generally degrades the efficiency and effectiveness of evolutionary algorithms. To overcome this source of difficulty, we designed an EDA that builds and samples multiple probabilistic models at each generation. Different from previous studies of globally multimodal problems that also use multiple models, we adopt multivariate probabilistic models. Furthermore, we have also devised a mechanism to automatically estimate the number of models that should be employed. The empirical results demonstrate that our approach obtains more global optima per run compared to the well-known EDA that employs the same class of probabilistic models but builds a single model at each generation. Moreover, the experiments also suggest that using multiple models reduces the generations spent to reach convergence.