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Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence: Studies in Computational Intelligence, cartea 21

Autor Candida Ferreira
en Limba Engleză Hardback – 24 mai 2006
Cândida Ferreira thoroughly describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. This monograph provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book also includes a self-contained introduction to this new exciting field of computational intelligence, including several new algorithms for decision tree induction, data mining, classifier systems, function finding, polynomial induction, times series prediction, evolution of linking functions, automatically defined functions, parameter optimization, logic synthesis, combinatorial optimization, and complete neural network induction. The book also discusses some important and controversial evolutionary topics that might be refreshing to both evolutionary computer scientists and biologists.
This second edition has been substantially revised and extended with five new chapters, including a new chapter describing two new algorithms for inducing decision trees with nominal and numeric/mixed attributes.
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Specificații

ISBN-13: 9783540327967
ISBN-10: 3540327967
Pagini: 504
Ilustrații: XX, 480 p.
Dimensiuni: 155 x 235 x 32 mm
Greutate: 0.88 kg
Ediția:2nd ed. 2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Introduction: The Biological Perspective.- The Entities of Gene Expression Programming.- The Basic Gene Expression Algorithm.- The Basic GEA in Problem Solving.- Numerical Constants and the GEP-RNC Algorithm.- Automatically Defined Functions in Problem Solving.- Polynomial Induction and Time Series Prediction.- Parameter Optimization.- Decision Tree Induction.- Design of Neural Networks.- Combinatorial Optimization.- Evolutionary Studies.

Textul de pe ultima copertă

Cândida Ferreira thoroughly describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. This monograph provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book also includes a self-contained introduction to this new exciting field of computational intelligence, including several new algorithms for decision tree induction, data mining, classifier systems, function finding, polynomial induction, times series prediction, evolution of linking functions, automatically defined functions, parameter optimization, logic synthesis, combinatorial optimization, and complete neural network induction. The book also discusses some important and controversial evolutionary topics that might be refreshing to both evolutionary computer scientists and biologists.
This second edition has been substantially revised and extended with fivenew chapters, including a new chapter describing two new algorithms for inducing decision trees with nominal and numeric/mixed attributes.
 
Cândida Ferreira thoroughly describes the basic ideas of gene
expression programming (GEP) and numerous modifications to this
powerful new algorithm. This monograph provides all the implementation
details of GEP so that anyone with elementary programming
skills will be able to implement it themselves. The book also includes a
self-contained introduction to this new exciting field of computational
intelligence, including several new algorithms for decision tree
induction, data mining, classifier systems, function finding, polynomial
induction, times series prediction, evolution of linking functions,
automatically defined functions, parameter optimization, logic
synthesis, combinatorial optimization, and complete neural network
induction. Thebook also discusses some important and controversial
evolutionary topics that might be refreshing to both evolutionary
computer scientists and biologists. This second edition has been
substantially revised and extended with five new chapters, including
a new chapter describing two new algorithms for inducing decision
trees with nominal and numeric/mixed attributes.

Caracteristici

Presents an exciting new development out of Genetic Algorithms Includes supplementary material: sn.pub/extras