Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms
based on the model of natural evolution, are compared in this article: evolution strategies …

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 …

Many seemingly different problems in machine learning, artificial intelligence, and symbolic
processing can be viewed as requiring the discovery of a computer program that produces …

Genetic algorithms are founded upon the principle of evolution, ie, survival of the fittest.
Hence evolution programming techniques, based on genetic algorithms, are applicable to …

One approach to the design of learning systems is to extract heuristics from existing adaptive
systems. Genetic algorithms are heuristic learning models based on principles drawn from …

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 …

From the Publisher: With the publication of this book, Hans-Paul Schwefel has responded to
rapidly growing interest in Evolutionary Computation, a field that originated, in part, with his …

Metaheuristics are the most exciting development in approximate optimization techniques of
the last two decades. They have had widespread successes in attacking a variety of difficult …

Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence
(AI) methods to build comprehensive AI systems. Describes major AI problems (pattern …

A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …