Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Convergence phases, variance trajectories, and runtime analysis of continuous EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The equation for response to selection and its use for prediction
Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
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This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). This proposal computes a Gaussian model which approximates a Boltzmann distribution via the minimization of the Kullback Leibler divergence. The resulting approach needs only one parameter: the population size. A set of problems is presented to show advantages and comparative performance of this approach with state of the art continuous EDAs.