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Springer Gene Expression Programming: Mathematical Modeling by AI

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Description

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence is a comprehensive guide to GEP algorithms and their computational applications. Authored by Candida Ferreira and published by Springer as part of the Studies in Computational Intelligence series (volume 21), this monograph delivers complete implementation details suitable for programmers with basic coding experience. The book introduces gene expression programming fundamentals alongside practical modifications to this powerful algorithm, covering decision tree induction, data mining, classifier systems, function finding, polynomial induction, and time series prediction. This resource bridges theoretical concepts with hands-on guidance, enabling readers to understand and apply evolutionary computation techniques across diverse machine learning and artificial intelligence projects.

Key Features

Thorough exploration of gene expression programming theory and modifications written by expert Candida Ferreira for computational intelligence professionals.

Complete implementation details and step-by-step algorithms designed for programmers with elementary programming skills to build GEP systems.

Covers multiple applications including decision tree induction, data mining, classifier systems, function finding, and polynomial induction techniques.

Published by Springer in the Studies in Computational Intelligence series (volume 21) as an authoritative academic reference.

Self-contained introduction to computational intelligence that includes time series prediction and evolution of linking functions methodology.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
24 May 2006
Listed Since
04 January 2007

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