Learning Probability Distributions in Continuous Evolutionary Algorithms - a Comparative Review

Learning Continuous Probability Distributions with Symmetric Diffusion Networks

in this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive propagation of information. Using methods of Markovlon diffusion theory, we formalize the activation dynamics of these networks and then show that they can be trained to reproduce entire muitivariote probability distributions an their ...

Probability Distributions: Discrete vs. Continuous

Formalization of Continuous Probability Distributions

Continuous probability distributions are widely used to mathematically describe random phenomena in engineering and physical sciences. In this paper, we present a methodology that can be used to formalize any continuous random variable for which the inverse of the cumulative distribution function can be expressed in a closed mathematical form. Our methodology is primarily based on the Standard ...

Marginal Distributions in Evolutionary Algorithms

In this paper, the description of two gene pool recombination operators is described. Both operators are based on the estimation of the distribution of the parents and its use to generate new individuals. The Univariate Marginal Distribution Algorithm (UMDA) uses simple univariate distributions. It is a perfect algorithm for linear problems. The Bivariate Marginal Distribution Algorithm (BMDA) ...

Learning Probability Distributions

Learning Probability Distributions