An Introduction to Genetic Algorithms - Boente

An Introduction to Genetic Algorithms Mitchell Melanie A Bradford Book The MIT Press Cambridge, Massachusetts • London, England Fifth printing, 1999 First MIT Press paperback edition, 1998 Copyright © 1996 Massachusetts Institute of Technology All rights reserved. No part of this publication may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Set in Palatino by Windfall Software using ZzT E X. Library

of Congress Cataloging−in−Publication Data Mitchell, Melanie. An introduction to genetic algorithms / Melanie Mitchell. p. cm. "A Bradford book." Includes bibliographical references and index. ISBN 0−262−13316−4 (HB), 0−262−63185−7 (PB) 1. Genetics—Computer simulation.2. Genetics—Mathematical models.I. Title. QH441.2.M55 1996 575.1'01'13—dc20 95−24489 CIP 1 Table of Contents An Introduction to Genetic Algorithms............................................................................................................1 Mitchell Melanie......................................................................................................................................1 Chapter 1: Genetic Algorithms: An Overview.................................................................................................2 Overview..................................................................................................................................................2 1.1 A BRIEF HISTORY OF EVOLUTIONARY COMPUTATION.....................................................2 1.2 THE APPEAL OF EVOLUTION.....................................................................................................4 1.3 BIOLOGICAL TERMINOLOGY.....................................................................................................5 1.4 SEARCH SPACES AND FITNESS LANDSCAPES.......................................................................6 1.5 ELEMENTS OF GENETIC ALGORITHMS...................................................................................7 Examples of Fitness Functions...................................................................................................7 GA Operators..............................................................................................................................8 1.6 A SIMPLE GENETIC ALGORITHM..............................................................................................8 1.7 GENETIC ALGORITHMS AND TRADITIONAL SEARCH METHODS..................................10 1.9 TWO BRIEF EXAMPLES..............................................................................................................12 Using GAs to Evolve Strategies for the Prisoner's Dilemma...................................................13 Hosts and Parasites: Using GAs to Evolve Sorting Networks..................................................16 1.10 HOW DO GENETIC ALGORITHMS WORK?...........................................................................21 THOUGHT EXERCISES......................................................................................................................23 COMPUTER EXERCISES...................................................................................................................24 Chapter 2: Genetic Algorithms in Problem Solving......................................................................................27 Overview................................................................................................................................................27 2.1 EVOLVING COMPUTER PROGRAMS.......................................................................................27 Evolving Lisp Programs...........................................................................................................27 Evolving Cellular Automata.....................................................................................................34 2.2 DATA ANALYSIS AND PREDICTION.......................................................................................42 Predicting Dynamical Systems.................................................................................................42 Predicting Protein Structure......................................................................................................47 2.3 EVOLVING NEURAL NETWORKS............................................................................................49 Evolving Weights in a Fixed Network.....................................................................................50 Evolving Network Architectures..............................................................................................53 Direct Encoding........................................................................................................................54 Grammatical Encoding.............................................................................................................55 Evolving a Learning Rule.........................................................................................................58 THOUGHT EXERCISES......................................................................................................................60 COMPUTER EXERCISES...................................................................................................................62 Chapter 3: Genetic Algorithms in Scientific Models.....................................................................................65 Overview................................................................................................................................................65 3.1 MODELING INTERACTIONS BETWEEN LEARNING AND EVOLUTION...........................66 The Baldwin Effect...................................................................................................................66 A Simple Model of the Baldwin Effect....................................................................................68 Evolutionary Reinforcement Learning.....................................................................................72 3.2 MODELING SEXUAL SELECTION.............................................................................................75 Simulation and Elaboration of a Mathematical Model for Sexual Selection...........................76 3.3 MODELING ECOSYSTEMS.........................................................................................................78 3.4 MEASURING EVOLUTIONARY ACTIVITY.............................................................................81 Thought Exercises..................................................................................................................................84 Computer Exercises...............................................................................................................................85 Table of Contents Chapter 4: Theoretical Foundations of Genetic Algorithms........................................................................87 Overview................................................................................................................................................87 4.1 SCHEMAS AND THE TWO−ARMED BANDIT PROBLEM.....................................................87 The Two−Armed Bandit Problem............................................................................................88 Sketch of a Solution..................................................................................................................89 Interpretation of the Solution....................................................................................................91 Implications for GA Performance.............................................................................................92 Deceiving a Genetic Algorithm................................................................................................93 Limitations of "Static" Schema Analysis..................................................................................93 4.2 ROYAL ROADS.............................................................................................................................94 Royal Road Functions...............................................................................................................94 Experimental Results................................................................................................................95 Steepest−ascent hill climbing (SAHC).....................................................................................96 Next−ascent hill climbing (NAHC)..........................................................................................96 Random−mutation hill climbing (RMHC)...............................................................................96 Analysis of Random−Mutation Hill Climbing.........................................................................97 Hitchhiking in the Genetic Algorithm......................................................................................98 An Idealized Genetic Algorithm...............................................................................................99 4.3 EXACT MATHEMATICAL MODELS OF SIMPLE GENETIC ALGORITHMS.....................103 Formalization of GAs.............................................................................................................103 Results of the Formalization...................................................................................................108 A Finite−Population Model....................................................................................................108 4.4 STATISTICAL−MECHANICS APPROACHES.........................................................................112 THOUGHT EXERCISES....................................................................................................................114 COMPUTER EXERCISES.................................................................................................................116 5.1 WHEN SHOULD A GENETIC ALGORITHM BE USED?........................................................116 5.2 ENCODING A PROBLEM FOR A GENETIC ALGORITHM...................................................117 Binary Encodings....................................................................................................................117 Many−Character and Real−Valued Encodings......................................................................118 Tree Encodings.......................................................................................................................118 5.3 ADAPTING THE ENCODING....................................................................................................118 Inversion.................................................................................................................................119 Evolving Crossover "Hot Spots"............................................................................................120 Messy Gas...............................................................................................................................121 5.4 SELECTION METHODS.............................................................................................................124 Fitness−Proportionate Selection with "Roulette Wheel" and "Stochastic Universal" Sampling................................................................................................................................124 Sigma Scaling.........................................................................................................................125 Elitism.....................................................................................................................................126 Boltzmann Selection...............................................................................................................126 Rank Selection........................................................................................................................127 Tournament Selection.............................................................................................................127 Steady−State...

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