designing edas by using the elitist convergent eda concept and the boltzmann distribution
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.
boltzmann distribution estimation of distribution algorithms performance analysis kullback-leibler divergence