Sentence examples for marginal distribution algorithm from inspiring English sources

The Univariate Marginal Distribution Algorithm (UMDA) – a popular estimation-of-distribution algorithm – is studied from a run time perspective.

This paper introduces a new approach for estimation of distribution algorithms called the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA).

The paper represents the approach to evolutionary analogue circuit design on the base of the univariate marginal distribution algorithm.

Wright's equation is only approximately valid in population genetics, but it exactly describes the behavior of our univariate marginal distribution algorithm (UMDA).

And a multi-objective heuristic algorithm called MOEDA is proposed to solve the model, which is an improvement of Univariate Marginal Distribution Algorithm.

In this paper, we propose the employment of a novel stochastic algorithm called Boltzmann Univariate Marginal Distribution Algorithm (BUMDA, Valdez, S. I. et al., 2013) coupled with self-adaptive handling constraints technique to optimize a well-known distillation process scheme.

Moreover, all the steganalytic algorithms using the first-order statistics of cover image are not efficient because the marginal distribution is inherently preserved by the algorithm.

Given a factor graph, the algorithm calculates the marginal distribution for each unobserved node conditioned by any nodes observed.

An existing estimation distribution algorithm (EDA) with univariate marginal Gaussian model was improved by designing and incorporating an extreme elitism selection method.

Over a sufficiently large number of iterations, the algorithm converges to the marginal distribution of the parameters, see [ 33, 69].

The age at event for each subject in each source population was generated from a standard Cox model with time-dependent covariates, using a permutation algorithm described elsewhere and assuming Weibull marginal distribution of age at event [ 4, 22, 23].