Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space
ECML '97 Proceedings of the 9th European 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
Multiobjective hBOA, clustering, and scalability
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Clustering-Based Probabilistic Model Fitting in Estimation of Distribution Algorithms
IEICE - Transactions on Information and Systems
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Maintaining Diversity in EDAs for Real-Valued Optimisation Problems
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Clustering and learning Gaussian distribution for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Hi-index | 0.00 |
Estimation of Distribution Algorithms (EDAs) are evolutionary optimization methods that build models which estimate the distribution of promising regions in the search space. Conventional EDAs use only one single model at a time. One way to efficiently explore multiple areas of the search space is to use multiple models in parallel. In this paper, we present a general framework for both single- and multimodel EDAs. We propose the use of clustering to divide selected individuals into different groups, which are then utilized to build separate models. For the multi-model case, we introduce the concept of model recombination. This novel framework has great generality, encompassing the traditional Evolutionary Algorithm and the EDA as its extreme cases. We instantiate our framework in the form of a real-valued algorithm and apply this algorithm to some well-known benchmark functions. Numerical results show that both single- and multi-model EDAs have their own strengths and weaknesses, and that the multi-model EDA is able to prevent premature convergence.