Genetic Systems Programming (eBook, PDF)

Theory and Experiences
Redaktion: Abraham, Ajith

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 234
• Erscheinungstermin: 21. Juli 2008
• Englisch
• ISBN-13: 9783540324980
• Artikelnr.: 44132771

Autorenporträt

Nadia Nedjah, State University of Rio de Janeiro, Brazil / Ajith Abraham, Chung-Ang University, Korea / Luiza de Macedo Mourelle, State University of Rio de Janeiro, Brazil

Inhaltsangabe

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.