Table of contents

  1. Front Matter
    Pages i-xv
  2. Introduction

  3. Fundamental Analysis

  4. Method Design

  5. Advanced Techniques and Phenomena

  6. Back Matter
    Pages 291-320

About this book

Introduction

Linear Genetic Programming examines the evolution of imperative computer programs written as linear sequences of instructions. In contrast to functional expressions or syntax trees used in traditional Genetic Programming (GP), Linear Genetic Programming (LGP) employs a linear program structure as genetic material whose primary characteristics are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. This volume investigates typical GP phenomena such as non-effective code, neutral variations and code growth from the perspective of linear GP.

The text is divided into three parts, each of which details methodologies and illustrates applications. Part I introduces basic concepts of linear GP and presents efficient algorithms for analyzing and optimizing linear genetic programs during runtime. Part II explores the design of efficient LGP methods and genetic operators inspired by the results achieved in Part I. Part III investigates more advanced techniques and phenomena, including effective step size control, diversity control, code growth, and neutral variations.

The book provides a solid introduction to the field of linear GP, as well as a more detailed, comprehensive examination of its principles and techniques. Researchers and students alike are certain to regard this text as an indispensable resource.

Keywords

Step Size Control Syntax algorithms code growth diversity control evolutionary algorithm genetic algorithms genetic operators genetic programming learning linear genetic programming machine learning neutral variations optimization programming

Authors and affiliations

  • Markus F. Brameier
    • 1
  • Wolfgang Banzhaf
    • 2
  1. 1.Bioinformatics Research Center (BiRC)University of AarhusDenmark
  2. 2.Department of Computer ScienceMemorial University of NewfoundlandSt. John’sCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-31030-5
  • Copyright Information Springer Science+Business Media, LLC 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-31029-9
  • Online ISBN 978-0-387-31030-5
  • Series Print ISSN 1932-0167
  • About this book