Tutorial on Genetic Algorithms

Genetic Algorithms – a Tutorial

Genetic Algorithms in Chemical Engineering: A Tutorial

Over the last two decades, genetic algorithms (GAs) have been successfully applied in many fields, such as engineering, robotics, economics, chemistry, genetics, operations research, art, and social sciences. GAs are stochastic search techniques that seek improved performance by sampling promising regions of the solution space, i.e., regions with a high probability for leading to good solutions...

A Tutorial on MM Algorithms

Most problems in frequentist statistics involve optimization of a function such as a likelihood or a sum of squares. EM algorithms are among the most effective algorithms for maximum likelihood estimation because they consistently drive the likelihood uphill by maximizing a simple surrogate function for the loglikelihood. Iterative optimization of a surrogate function as exemplified by an EM al...

Multi-objective optimization using genetic algorithms: A tutorial

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investig...

A tutorial review on Text Mining Algorithms

As we enter the third decade of the World Wide Web (WWW), the textual revolution has seen a tremendous change in the availability of online information. Finding information for just about any need has never been more automatic—just a keystroke or mouse click away .It can be viewed as one of a class of non traditional Information Retrieval (IR) strategies which attempt to treat entire text colle...