Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence: 21 (Studies in Computational Intelligence) - Softcover

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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.

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.