£108.00

Springer - Linear Genetic Programming Book for AI Researchers

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Description

Explore a specialized variant of Genetic Programming with Linear Genetic Programming by Springer. This text examines how to evolve imperative computer programs as linear sequences of instructions. Unlike traditional methods that rely on functional expressions or syntax trees, this approach offers a different perspective on how programs are built and optimized. This book provides a deep dive into typical GP phenomena, including non-effective code, neutral variations, and code growth. It is designed to be a comprehensive reference for researchers working in AI and machine learning, while also providing the necessary introductory material for students and newcomers entering the field. Whether you are studying evolutionary computation or developing advanced machine learning algorithms, this book serves as a foundational resource for understanding linear instruction sequences in program evolution.

Key Features

Focuses on evolving imperative computer programs using linear sequences of instructions instead of traditional syntax trees.

Provides a detailed investigation into GP phenomena such as code growth, neutral variations, and non-effective code.

Serves as a professional reference guide for researchers in the field of genetic and evolutionary computation.

Includes foundational introductory material to help students and newcomers understand complex evolutionary concepts.

Offers a unique perspective on program evolution compared to standard functional expression methods.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
29 November 2010
Listed Since
14 June 2010

Barcode

No barcode data available

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