Improved Estimation of Distribution Algorithms Based on Gaussian Distribution

Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms

Multivariate Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for Multivariate Gaussian based EDAs (MGEDAs), named Eigen Decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen analysis of the covariance matrix, and i...

Estimation of Distribution Algorithms

A Survey of Estimation of Distribution Algorithms Based on Copulas

The use of probabilistic models based on copulas in Estimation of Distribution Algorithms (EDAs) has been identi ed as an emerging research trend on these algorithms for continuous domains. By using copulas, the e ect of the dependence structure and the margins in a joint distribution can be represented separately. Consequently, EDAs based on copulas inherit these characteristics and are able t...

Software reliability prediction model based on support vector regression with improved estimation of distribution algorithms

Software reliability prediction plays a very important role in the analysis of software quality and balance of software cost. The data during software lifecycle is used to analyze and predict software reliability. However, predicting the variability of software reliability with time is very difficult. Recently, support vector regression (SVR) has been widely applied to solve nonlinear predictin...

Parallel estimation of distribution algorithms

The reduction in the execution time is a factor that becomes very important when using many applications nowadays. In spite of the enhancement on computer hardware, this problem is always happening, as the increase in power is always related to the application of new methods that were also too time consuming in previous computer systems. Parallel programming techniques provide feasibility of so...