Table Of ContentRepresentations forGeneticandEvolutionary Algorithms
Franz Rothlauf
Representations for Genetic
and Evolutionary Algorithms
ABC
Dr.FranzRothlauf
SchoolofBusiness
Administration
UniversitätMannheim
68131Mannheim
Germany
E-mail:[email protected]
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ISBN-10 3-540-25059-XSpringerBerlinHeidelbergNewYork
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Fu¨r meine Eltern Elisabeth und Alfons Rothlauf.
Preface
Preface to the Second Edition
Ihavebeenverypleasedtoseehowwellthefirsteditionofthisbookhasbeen
acceptedandusedbyitsreaders.Ihavereceivedfantasticfeedbacktellingme
that people use it as an inspiration for their own work, give it to colleagues
orstudents,oruseitforpreparinglecturesandclassesaboutrepresentations.
I want to thank you all for using the material presented in this book and for
developing more efficient and powerful heuristic optimization methods.
You will find this second edition of the book completely revised and ex-
tended. The goal of the revisions and extensions was to make it easier for the
reader to understand the main points and to get a more thorough knowledge
ofthedesignofhigh-qualityrepresentations.Forexample,Iwanttodrawyour
attention to Chap. 3 where you find the core of the book. I have extended
and improved the sections about redundancy and locality of representations
adding new material and experiments and trying to draw a more compre-
hensive picture. In particular, the introduction of synonymity for redundant
encodingsinSect.3.1andtheintegrationoflocalityandredundancyissuesin
Sect. 3.3 are worth having a closer look at it. These new concepts have been
used throughout the work and have made it possible to better understand a
variety of different representation issues.
The chapters about tree representations have been reorganized such that
they explicitly distinguish between direct and indirect representations. This
distinction – including a new analysis of the edge-sets, which is a direct en-
coding for trees – emphasizes that the developed representation framework
is not only helpful for analysis and design of representations, but also for
operators. The design of proper search operators is at the core of direct rep-
resentations and the new sections demonstrate how to analyze the influence
of such encodings on the performance of genetic and evolutionary algorithms
(GEAs). Finally, the experiments presented in Chap. 8 have been completely
revised considering new representations and giving a better understanding of
the influence of tree representations on the performance of GEAs.
VIII Preface
Iwouldliketotakethisopportunitytothankeveryonewhotookthetime
tosharetheirthoughtsonthetextwithme–allthesecommentswerehelpful
in improving the book. Special thanks to Kati for her support in preparing
this work.
As with the first edition, my purpose will be fulfilled if you find this book
helpful for building more efficient heuristic optimization methods, if you find
itinspiringforyourresearch,orifitisahelpforyouteachingstudentsabout
the importance and influence of representations.
Mannheim
August 2005 Franz Rothlauf
Preface to the First Edition
This book is about representations for genetic and evolutionary algorithms
(GEAs). In writing it, I have tried to demonstrate the important role of
representations for an efficient use of genetics and evolutionary optimization
methods. Although, experience often shows that the choice of a proper repre-
sentation is crucial for GEA’s success, there are few theoretical models that
describe how representations influence GEAs behavior. This book aims to re-
solve this unsettled situation. It presents theoretical models describing the
effect of different types of representations and applies them to binary repre-
sentations of integers and tree representations.
Thebookisdesignedforpeoplewhowanttolearnsometheoryabouthow
representationsinfluenceGEAperformanceandforthosewhowanttoseehow
this theory can be applied to representations in the real world. The book is
basedonmydissertationwiththetitle“TowardsaTheoryofRepresentations
forGeneticandEvolutionaryAlgorithms:DevelopmentofBasicConceptsand
their Application to Binary and Tree Representations”. To make the book
easier to read for a larger audience some chapters are extended and many
explanations are more detailed. During the writing of the book many people
fromvariousbackgrounds(economics,computerscienceandengineering)had
alookattheworkandpushedmetopresentitinawaythatisaccessibletoa
diverseaudience.Therefore,alsopeoplethatarenotfamiliartoGEAsshould
be able to get the basic ideas of the book.
Tounderstandthetheoreticalmodelsdescribingtheinfluenceofrepresen-
tations on GEA performance I expect college-level mathematics like elemen-
tary notions of counting, probability theory and algebra. I tried to minimize
the mathematical background required for understanding the core lessons of
the book and to give detailed explanations on complex theoretical subjects.
Furthermore, I expect the reader to have no particular knowledge of genetics
and define all genetic terminology and concepts in the text. The influence of
Preface IX
integerandtreerepresentationsonGEAperformancedoesnotnecessarilyre-
quire a complete understanding of the elements of representation theory but
is also accessible for people who do not want to bother too much with theory.
The book is split up into two large parts. The first presents theoretical
models describing the effects of representations on GEA performance. The
second part uses the theory for the analysis and design of representations.
After the first two introductory chapters, theoretical models are presented on
how redundant representations, exponentially scaled representations and the
locality/distance distortion of a representation influence GEA performance.
In Chap. 4 the theory is used for formulating a time-quality framework. Con-
sequently, in Chap. 5, the theoretical models are used for analyzing the per-
formance differences between binary representations of integers. Finally, the
framework is used in Chap. 6, Chap. 7, and Chap. 8 for the analysis of exist-
ing tree representations as well as the design of new tree representations. In
theappendixcommontestinstancesfortheoptimalcommunicationspanning
tree problems are summarized.
Acknowledgments
First of all, I would like to thank my parents for always providing me with
a comfortable home environment. I have learned to love the wonders of the
world and what the important things in life are.
Furthermore, I would like to say many thanks to my two advisors, Dr.
Armin Heinzl and Dr. David E. Goldberg. They did not only help me a lot
with my work, but also had a large impact on my private life. Dr. Armin
Heinzl helped me to manage my life in Bayreuth and always guided me in
the right direction in my research. He was a great teacher and I was able to
learn many important things from him. I am grateful to him for creating an
environmentthatallowedmetowritethisbook.Dr.DavidE.Goldberghada
largeinfluenceonmyresearchlife.HetaughtmemanythingswhichIneeded
in my research and I would never have been able to write this thesis without
his help and guidance.
During my time here in Bayreuth, my colleagues in the department
have always been a great help to overcome the troubles of daily university
life. I especially want to thank Michael Zapf, Lars Brehm, Jens Dibbern,
Monika Fortmu¨hler, Torsten O. Paulussen, Ju¨rgen Gerstacker, Axel Pu¨rck-
hauer, Thomas Schoberth, Stefan Hocke, and Frederik Loos. During my time
here, Wolfgang Gu¨ttler and Tobias Grosche were not only work colleagues,
but also good friends. I want to thank them for the good time I had and the
interesting discussions.
During the last three years during which I spent time at IlliGAL I met
manypeoplewhohavehadagreatimpactonmylife.Firstofall,Iwouldlike
to thank David E. Goldberg and the Department of General Engineering for
givingmetheopportunitytostaytheresooften.Then,Iwanttosaythankyou
X Preface
tothefolksatIlliGALIwasabletoworktogetherwith.Itwasalwaysareally
great pleasure. I especially want to thank Erick Cantu´-Paz, Fernando Lobo,
DimitriKnjazew,ClarissavanHoyweghen,MartinButz,MartinPelikan,and
Kumara Sastry. It was not only a pleasure working together with them but
over time they have become really good friends. My stays at IlliGAL would
not have been possible without their help.
Finally,Iwanttothankthepeoplewhowereinvolvedinthewritingofthis
book. First of all I want to thank Kumara Sastry and Martin Pelikan again.
They helped me a lot and had a large impact on my work. The discussions
with Martin were great and Kumara often impressed me with his expansive
knowledge about GEAs. Then, I want to say thanks to Fernando Lobo and
Torsten O. Paulussen. They gave me great feedback and helped me to clarify
my thoughts. Furthermore, Katrin Appel and Kati Sternberg were a great
help in writing this dissertation. Last but not least I want to thank Anna
Wolf. Anna is a great proofreader and I would not have been able to write a
book in readable English without her help.
Finally,Iwanttosay“thankyou”toKati.NowIwillhopefullyhavemore
time for you.
Bayreuth
January 2002 Franz Rothlauf
Foreword to the First Edition
It is both personally and intellectually pleasing for me to write a foreword
to this work. In January 1999 I received a brief e-mail from a PhD student
at the University of Bayreuth asking if he might visit the Illinois Genetic Al-
gorithms Laboratory (IlliGAL). I did not know this student, Franz Rothlauf,
but something about the tone of his note suggested a sharp, eager mind con-
nected to a cheerful, positive nature. I checked out Franz’s references, invited
himtoIllinoisforafirstvisit,andmyearlyfeelingsweresoonprovencorrect.
Franz’s various visits to the lab brought both smiles to the faces of IlliGAL
labbies and important progress to a critical area of genetic algorithm inquiry.
It was great fun to work with Franz and it was exciting to watch this work
takeshape.Intheremainder,Ibrieflyhighlightthecontributionsofthiswork
to our state of knowledge.
In the field of genetic and evolutionary algorithms (GEAs), much theory
and empirical study has been heaped upon operators and test problems, but
problem representation has often been taken as a given. In this book, Franz
breaks with this tradition and seriously studies a number of critical elements
of a theory of GEA representation and applies them to the careful empirical
study of (a) a number of important idealized test functions and (b) problems
of commercial import. Not only is Franz creative in what he has chosen to
study, he also has been innovative in how he performs his work.
In GEAs – as elsewhere – there appears sometimes to be a firewall sepa-
rating theory and practice. This is not new, and even Bacon commented on
this phenomenon with his famous metaphor of the spiders (men of dogmas),
the ants (men of practice), and the bees (transformers of theory to practice).
In this work, Franz is one of Bacon’s bees, taking applicable theory of rep-
resentation and carrying it to practice in a manner that (1) illuminates the
theory and (2) answers the questions of importance to a practitioner.
This book is original in many respects, so it is difficult to single out any
one of its many accomplishments. I do believe five items deserve particular
comment:
1. Decomposition of the representation problem
2. Analysis of redundancy
XII Foreword to the First Edition
3. Analysis of scaling
4. Time-quality framework for representation
5. Demonstration of the framework in well-chosen test problems and prob-
lems of commercial import.
Franz’s decomposition of the problem of representation into issues of re-
dundancy, scaling, and correlation is itself a contribution. Individuals have
isolated each of these areas previously, but this book is the first to suggest
they are core elements of an integrated theory and to show the way toward
that integration.
The analyses of redundancy and scaling are examples of applicable or
facetwise modeling at its best. Franz gets at key issues in run duration and
populationsizethroughboundinganalyses,andthesepermithimtodrawdef-
initeconclusionsinfieldswheresomanyotherresearchershavesimplywaived
their arms.
Bythemselves,theseanalyseswouldbesufficient,butFranzthentakesthe
extra and unprecedented step toward an integrated quality-time framework
for representations. The importance of quality and time has been recognized
previously from the standpoint of operator design, but this work is the first
to understand that codings can and should be examined from an efficiency-
quality standpoint as well. In my view, this recognition will be understood
in the future as a key turning point away from the current voodoo and black
magic of GEA representation toward a scientific discussion of the appropri-
ateness of particular representations for different problems.
Finally, Franz has carefully demonstrated his ideas in (1) carefully chosen
test functions and (2) problems of commercial import. Too often in the GEA
field, researchers perform an exercise in pristine theory without relating it to
practice. On the other hand, practitioners too often study the latest wrinkle
in problem representation or coding without theoretical backing or support.
This dissertation asserts the applicability of its theory by demonstrating its
utility in understanding tree representations, both test functions and real-
world communications networks. Going from theory to practice in such a
sweeping manner is a rare event, and the accomplishment must be regarded
as both a difficult and an important one.
AllthiswouldbeenoughformetorecommendthisbooktoGEAaficiona-
dos around the globe, but I hasten to add that the book is also remarkably
well written and well organized. No doubt this rhetorical craftsmanship will
help broaden the appeal of the book beyond the ken of genetic algorithmists
andcomputationalevolutionaries.Inshort,Irecommendthisimportantbook
to anyone interested in a better quantitative and qualitative understanding
of the representation problem. Buy this book, read it, and use its important
methodological, theoretical, and practical lessons on a daily basis.
University of Illinois at Urbana-Champaign David E. Goldberg
