ESTIMATION OF DISTRIBUTION ALGORITHMS FRAMEWORK

Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates of a probabilistic model, starting with the model encoding the uniform distribution over admissible solutions and ending with the model that generates only the global optima.

This framework is developed by Karlo Knezevic. It is written as practical part of Master Thesis in University of Zagreb, faculty of Electrical Engineering and Computing.

Purpose of this framework is EDA development and comparing with other evolutionary or stohastic or machine learning algorithms.

User package is workenvironment and there each class must Evaluation class extend.

@author Karlo Knezevic, [email protected] @version 1.1