Linear Genetic Programming
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 (
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Genetic and Evolutionary Computation Series Series Editors David E. Goldberg Consulting Editor IlliGAL, Dept. of General Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 USA Email: [email protected] John R. Koza Consulting Editor Medical Informatics Stanford University Stanford, CA 94305-5479 USA Email: [email protected]
Selected titles from this series: Nikolay Y. Nikolaev, Hitoshi Iba Adaptive Learning of Polynomial Networks, 2006 ISBN 978-0-387-31239-2 Tetsuya Higuchi, Yong Liu, Xin Yao Evolvable Hardware, 2006 ISBN 978-0-387-24386-3 David E. Goldberg The Design of Innovation: Lessons from and for Competent Genetic Algorithms, 2002 ISBN 978-1-4020-7098-3 John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Jessen Yu, Guido Lanza Genetic Programming IV: Routine Human-Computer Machine Intelligence ISBN: 978-1-4020-7446-2 (hardcover), 2003; ISBN: 978-0-387-25067-0 (softcover), 2005 Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont Evolutionary Algorithms for Solving Multi-Objective Problems, 2002 ISBN: 978-0-306-46762-2 Lee Spector Automatic Quantum Computer Programming: A Genetic Programming Approach ISBN: 978-1-4020-7894-1 (hardcover), 2004; ISBN 978-0-387-36496-4 (softcover), 2007 William B. Langdon Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! 1998 ISBN: 978-0-7923-8135-8
For a complete listing of books in this series, go to http://www.springer.com
Markus Brameier Wolfgang Banzhaf
Linear Genetic Programming
13
Markus F. Brameier Bioinformatics Research Center (BiRC) University of Aarhus Denmark [email protected] Wolfgang Banzhaf Department of Computer Science Memorial University of Newfoundland St. John’s, NL Canada [email protected]
Library of Congress Control Number: 2006920909
ISBN-10: 0-387-31029-0 ISBN-13: 978-0387-31029-9
e-ISBN-10: 0-387-31030-4 e-ISBN-13: 978-0387-31030-5
© 2007 by Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science + Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America
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Contents
Preface
xi
About the Authors
xv
1. INTRODUCTION
1
1.1
Evolutionary Algorithms
1
1.2
Genetic Programming
3
1.3
Linear Genetic Programming
6
1.4
Motivation
8
Part I
Fundamental Analysis
2. BASIC CONCEPTS OF LINEAR
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