Estimation of Distribution Algorithms for Function Optimization

Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms

In this chapter we focus on the design of real–valued EDAs for the task of numerical optimization. Here, both the problem variables as well as their encoding are real values. Concordantly, the type of probability distribution to be used for estimation and sampling in the EDA is continuous. In this chapter we indicate the main challenges in this area. Furthermore, we review the existing literatu...

Student's t Distribution based Estimation of Distribution Algorithms for Derivative-free Global Optimization

In this paper, we are concerned with a branch of evolutionary algorithms termed estimation of distribution (EDA), which has been successfully used to tackle derivative-free global optimization problems. For existent EDA algorithms, it is a common practice to use a Gaussian distribution or a mixture of Gaussian components to represent the statistical property of available promising solutions fou...

Estimation of Distribution Algorithms

Efficient Estimation of the Density and Cumulative Distribution Function of the Generalized Rayleigh Distribution

The uniformly minimum variance unbiased (UMVU), maximum likelihood, percentile (PC), least squares (LS) and weighted least squares (WLS) estimators of the probability density function (pdf) and cumulative distribution function are derived for the generalized Rayleigh distribution. This model can be used quite effectively in modelling strength data and also modeling general lifetime data. It has...

Estimation of Distribution Algorithms for Knapsack Problem

Estimation of distribution algorithms ( EDAs ) is a new kind of evolution algorithm. In EDAs , through the statistics of the information of selected individuals in current group, the probability of the individual distribution in next generation is given and the next generation of group is formed by random sampling. A wide range of mathematical model of the knapsack problem are proposed. In this...