Fr. 135.00

Genetic Systems Programming - Theory and Experiences

English · Paperback / Softback

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Description

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Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.

List of contents

Evolutionary Computation: from Genetic Algorithms to Genetic Programming.- Automatically Defined Functions in Gene Expression Programming.- Evolving Intrusion Detection Systems.- Evolutionary Pattern Matching Using Genetic Programming.- Genetic Programming in Data Modelling.- Stock Market Modeling Using Genetic Programming Ensembles.- Evolutionary Digital Circuit Design Using Genetic Programming.- Evolving Complex Robotic Behaviors Using Genetic Programming.- Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming.

About the author

Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Summary

Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.

Product details

Assisted by Ajit Abraham (Editor), Ajith Abraham (Editor), Luiza De Macedo Mourelle (Editor), Nadia Nedjah (Editor)
Publisher Springer, Berlin
 
Languages English
Product format Paperback / Softback
Released 13.10.2010
 
EAN 9783642067532
ISBN 978-3-642-06753-2
No. of pages 234
Dimensions 156 mm x 13 mm x 234 mm
Weight 393 g
Illustrations XXII, 234 p. 114 illus.
Series Studies in Computational Intelligence
Studies in Computational Intelligence
Subjects Natural sciences, medicine, IT, technology > Technology > General, dictionaries

C, Künstliche Intelligenz, Artificial Intelligence, Evolution, Operating System, Modeling, Robot, engineering, Programming, microcontroller, Mathematical and Computational Engineering, Mathematical and Computational Engineering Applications, Engineering—Data processing, genetic algorithms, genetic programming, evolutionary computation

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