Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence by Candida FerreiraGene Expression Programming: Mathematical Modeling by an Artificial Intelligence by Candida Ferreira

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence

byCandida Ferreira

Paperback | October 14, 2014

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C'ndida 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.

Title:Gene Expression Programming: Mathematical Modeling by an Artificial IntelligenceFormat:PaperbackDimensions:480 pages, 23.5 × 15.5 × 0.01 inPublished:October 14, 2014Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3642069320

ISBN - 13:9783642069321

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Table of Contents

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.