Stochastic simulation
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Management Science
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
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
The equation for response to selection and its use for prediction
Evolutionary Computation
Reinforcement learning estimation of distribution algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Finding global optima in the continuous domain is challenging for Genetic Algorithms (GAs). Traditional GAs use either binary-coded or real-coded representation of the variables, but there is a trade-off between these two encoding methods. Recombination operators for binary-coded GAs are simple to design, but the length of the string representing an individual would be huge if the number of design variables is larger; whereas, in real-coded GAs the length of an individual would be shorter, but the design of crossover and mutation operators are difficult, and sometimes they lead to premature convergence. To alleviate the problems of these two methods of encoding, real-coded Estimation of Distribution Algorithms (EDAs), which replace the recombination operators of GAs with estimation and sampling of the probability density function of the variables at each generation, have been proposed. In this paper, we show how real-coded EDAs can be applied to the optimization of multivariate functions in continuous domain and present the experimental results of three benchmark functions produced by our proposed algorithm. In comparison with others EDAs, our proposed method obtains encouraging accuracy and efficiency.