This paper introduces genetic algorithms (GA) as a complete entity, in which knowledge of
this emerging technology can be integrated together to form the framework of a design tool …

Evolutionary computation has started to receive significant attention during the last decade,
although the origins can be traced back to the late 1950's. This article surveys the history as …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

Genetic algorithms have been used in science and engineering as adaptive algorithms for
solving practical problems and as computational models of natural evolutionary systems …

A comprehensive guide to a powerful new analytical tool by two of its foremost innovators
The past decade has witnessed many exciting advances in the use of genetic algorithms …

Neural networks are a computing paradigm that is finding increasing attention among
computer scientists. In this book, theoretical laws and models previously scattered in the …

Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration
into the adaptive mechanisms that enable intelligent behaviour in complex and changing …

The issue of controlling values of various parameters of an evolutionary algorithm is one of
the most important and promising areas of research in evolutionary computation: it has a …

In this paper we introduce interval-schemata as a tool for analyzing real-coded genetic
algorithms (GAs). We show how interval-schemata are analogous to Holland's symbol …

A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this
article. The searching capability of genetic algorithms is exploited in order to search for …