Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice

134 Pages·2006·2.799 MB·English

by  Ying-ping Chen (auth.)

Ying-pingChen
ExtendingtheScalabilityofLinkageLearningGeneticAlgorithms
StudiesinFuzzinessandSoftComputing,Volume 190
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ISBN3-540-25322-X ExtendingtheScalabilityofLinkage
LearningGeneticAlgorithms,2006
ISBN3-540-28459-1
Ying-ping Chen
Extending the Scalability
of Linkage Learning
Genetic Algorithms
Theory & Practice
ABC
Ying-pingChen
NaturalComputingLaboratory
DepartmentofComputerScienceand
InformationEngineering
NationalChiaoTungUniversity
No.1001,DasyueRd.
HsinchuCity300
Taiwan
E-mail:
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ISBN-13 978-3-540-28459-8SpringerBerlinHeidelbergNewYork
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To my family

Foreword
It is a pleasure for me to write a foreword for Ying-ping Chen’s new book,
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory
and Practice. I first met Y.-p. when he asked to do an independent study
project with me in the spring of 2000 at the University of Illinois. He seemed
very interested in genetic algorithms based on some previous studies back
in Taiwan, and Georges Harik’s earlier work on the linkage learning genetic
algorithm (LLGA) interested him most of all.
In designing the LLGA, Harik attempted to reconcile the differing time
scales of allelic convergence and linkage convergence, by sustaining allelic di-
versity until linkage convergence occurred. The mechanism for achieving this
waselegant,andHarikalsoprovidedboundinganalysesthathelpedusunder-
standhowthemechanismachieveditsaims,buttheworkleftuswithasmany
questions as it answered. Why did the LLGA work so well on badly scaled
problems,andwhydiditseemtobesolimitedonuniformlyscaledproblems?
This was the state of our knowledge when Y.-p. tackled the problem.
Early attempts to improve upon the LLGA appeared to be dead ends,
and both of us were growing frustrated, but then we decided to break it
down into simpler elements, and Ying-ping made progress by performing an
enormously clever series of analyses and experiments that showed the way
to improved LLGA performance. In the end, the work has left us with a
better understanding of this particular mechanism and it has suggested that
unimetric schemes – schemes that do not use some auxiliary modeling metric
– may be limited in the performance levels they can achieve. Although the
goals of this work were to improve an artificial adaptation procedure, we
believe that it has important implications for the study of linkage adaptation
in nature.
Thus, I recommend this book to readers in either natural or artificial
systems, both for the important ideas that it clarifies and for its painstaking
VIII Foreword
methodofexperimentationandanalysis.Buythisbook,readit,andassimilate
itscrucialinsightsonthedifficultyandscalabilityofeffectivelinkagelearning.
Urbana, Illinois, USA David E. Goldberg
July, 2005
Preface
There are two primary objectives of this monograph. The first goal is to
identify certain limits of genetic algorithms that use only fitness for learning
genetic linkage. Both an explanatory theory and experimental results to sup-
port the theory are provided. The other goal is to propose a better design
of the linkage learning genetic algorithm. After understanding the cause of
the observed performance barrier, the design of the linkage learning genetic
algorithmismodifiedaccordinglytoimproveitsperformanceontheproblems
of uniformly scaled building blocks.
This book starts with presenting the background of the linkage learning
genetic algorithm. Then, it introduces the use of promoters on chromosomes
to improve the performance of the linkage learning genetic algorithm on uni-
formlyscaledproblems.Theconvergencetimemodelisconstructedbyidenti-
fyingthesequentialbehavior,developingthetightnesstimemodel,andestab-
lishingtheconnectioninbetween.Theuseofsubchromosomerepresentations
is to avoid the limit implied by the convergence time model. The experimen-
tal results suggest that the use of subchromosome representations may be a
promising way to design a better linkage learning genetic algorithm.
The study depicted in this monograph finds that using promoters on the
chromosome can improve nucleation potential and promote correct building-
block formation. It also observes that the linkage learning genetic algorithm
has a consistent, sequential behavior instead of different behaviors on differ-
ent problems as was previously believed. Moreover, the competition among
building blocks of equal salience is the main cause of the exponential growth
ofconvergencetime.Finally,adoptingsubchromosomerepresentationscanre-
duce the competition among building blocks, and therefore, scalable genetic
linkage learning for a unimetric approach is possible.
HsinChu City, Taiwan Ying-ping Chen
June, 2005