Journal of Global Optimization
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th 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
On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Clustering and learning Gaussian distribution for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Co-evolutionary differential evolution with dynamic population size and adaptive migration strategy
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A Boltzmann based estimation of distribution algorithm
Information Sciences: an International Journal
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Extending Estimation of distribution algorithms (EDAs) to the continuous field is a promising and challenging task. With a single probabilistic model, most existing continuous EDAs usually suffer from the local stagnation or a low convergence speed. This paper presents an enhanced continuous EDA with multiple probabilistic models (MP-EDA). In the MP-EDA, the population is divided into two subpopulations. The one involved by histogram model is used to roughly capture the global optima, whereas the other involved by Gaussian model is aimed at finding highly accurate solutions. During the evolution, a migration operation is periodically carried out to exchange some best individuals of the two subpopulations. Besides, the MP-EDA adaptively adjusts the offspring size of each subpopulation to improve the searching efficiency. The effectiveness of the MP-EDA is investigated by testing ten benchmark functions. Compared with several state-of-the-art evolutionary computations, the proposed algorithm can obtain better results in most test cases.