Genetic Programming. The Morgan Kaufmann Series in Artificial Intelligence

  • ID: 1762057
  • Book
  • 512 Pages
  • Elsevier Science and Technology
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Since the early 1990s, genetic programming (GP)-a discipline whose goal is to enable the automatic generation of computer programs-has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.

This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

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1 Genetic Programming as Machine Learning
2 Genetic Programming and Biology
3 Computer Science and Mathematical Basics
4 Genetic Programming as Evolutionary Computation
5 Basic Concepts-The Foundation
6 Crossover-The Center of the Storm
7 Genetic Programming and Emergent Order
8 Analysis-Improving Genetic Programming with Statistics
9 Different Varieties of Genetic Programming
10 Advanced Genetic Programming
11 Implementation-Making Genetic Programming Work
12 Applications of Genetic Programming
13 Summary and Perspectives
A Printed and Recorded Resources
B Information Available on the Internet
C GP Software
D Events
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Banzhaf, Wolfgang
Nordin, Peter
Keller, Robert E.
Francone, Frank D.
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