A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
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
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
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Connection Science - Evolutionary Learning and Optimisation
Expert Systems with Applications: An International Journal
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
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Estimation of Distribution Algorithms (EDAs) is an evolutionary computation optimization paradigm that relies the evolution of each generation on calculating a probabilistic graphical model able to reflect dependencies among variables out of the selected individuals of the population. This showed to be able to improve results with GAs for complex problems. This paper presents a new hybrid approach combining EDAs and particle swarm optimization, with the aim to take advantage of EDAs capability to learn from the dependencies between variables while profiting particle swarm's optimization ability to keep a sense of "direction" towards the most promising areas of the search space. Experimental results show the validity of this approach with widely known combinatorial optimization problems.