Table Of ContentStudies in Computational Intelligence 967
Diego Oliva
Essam H. Houssein
Salvador Hinojosa Editors
Metaheuristics
in Machine
Learning: Theory
and Applications
Studies in Computational Intelligence
Volume 967
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· ·
Diego Oliva Essam H. Houssein
Salvador Hinojosa
Editors
Metaheuristics in Machine
Learning: Theory
and Applications
Editors
DiegoOliva EssamH.Houssein
ComputerSciencesDepartment DepartmentofComputerScience
CUCEI FacultyofComputersandInformation
UniversityofGuadalajara,Guadajalara, MiniaUniversity
Jalisco,Mexico Minia,Egypt
SalvadorHinojosa
ComputerSciencesDepartment
CUCEI
UniversityofGuadalajara,Guadajalara,
Jalisco,Mexico
ISSN1860-949X ISSN1860-9503 (electronic)
StudiesinComputationalIntelligence
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Preface
In recent years, metaheuristics (MHs) have become important tools for solving
hardoptimizationproblemsencounteredinindustry,engineering,biomedical,image
processing,aswellasinthetheoreticalfield.Severaldifferentmetaheuristicsexist,
andnewonesareunderconstantdevelopment.Oneofthemostfundamentalprin-
ciplesinourworldisthesearchforanoptimalstate.Therefore,choosingtheright
solutionmethodforanoptimizationproblemcanbecruciallyimportantinfindingthe
rightsolutionsforagivenoptimizationproblem(unconstrainedandconstrainedopti-
mizationproblems).ThereexistadiverserangeofMHsforoptimization.Optimiza-
tionisanimportantanddecisiveactivityinscienceandengineering.Engineerswill
beabletoproducebetterdesignswhentheycansavetimeanddecreasetheproblem
complexitywithoptimizationmethods.Manyengineeringoptimizationproblemsare
naturallymorecomplexanddifficulttosolvebyconventionaloptimizationmethods
suchasdynamicprogramming.Inrecentyears,moreattentionhasbeenpaidtoinno-
vativemethodsderivedfromthenaturethatisinspiredbythesocialorthenatural
systems, which have yielded outstanding results in solving complex optimization
problems. Metaheuristic algorithms are a type of random algorithm which is used
tofindtheoptimalsolutions.Metaheuristicsareapproximatetypesofoptimization
algorithmsthatcanbetterescapefromthelocaloptimumpointsandcanbeusedin
awiderangeofengineeringproblems.
Recently,metaheuristics(MHs)andMachinelearning(ML)becameaveryimpor-
tant and hot topic to solve real-world applications in the industrial world, science,
engineering,etc.Amongthesubjectstobeconsideredaretheoreticaldevelopmentsin
MHs;performancecomparisonsofMHs;cooperativemethodscombiningdifferent
typesofapproachessuchasconstraintprogrammingandmathematicalprogramming
techniques; parallel and distributed MHs for multi-objective optimization; adapta-
tion of discrete MHs to continuous optimization; dynamic optimization; software
implementations; and real-life applications. Besides, Machine learning (ML) is a
data analytics technique to use computational methods. Therefore, recently, MHs
have been combined with several ML techniques to deal with different global and
engineeringoptimizationproblems,alsoreal-worldapplications.Chapterspublished
inthe“Metaheuristicsinmachinelearning:theoryandapplications(MAML2020)”
book describe original works in different topics in both science and engineering,
v
vi Preface
such as: Metaheuristics, Machine learning, Soft Computing, Neural Networks,
Multi-criteriadecisionmaking,energyefficiency,sustainabledevelopment,etc.
In short, it can be said that metaheuristic algorithms and machine learning are
advanced and general search strategies. Therefore, the main contribution of this
book is to indicate the advantages and importance of metaheuristics with machine
learninginvariousreal-worldapplications.
Guadajalara,Mexico DiegoOliva
Minia,Egypt EssamH.Houssein
Guadajalara,Mexico SalvadorHinojosa
Introduction
This book “MAML2020” collects several hybridized metaheuristics (MHs) with
machinelearning(ML)methodsforvariousreal-worldapplications.Hence,theMHs
havebecomeessentialtoolsforsolvinghardoptimizationproblemsencounteredin
industry,engineering,biomedical,imageprocessing,aswellasinthetheoreticalfield.
Besides,machinelearning(ML)isadataanalyticstechniquetousecomputational
methods.Therefore,recently,MHshavebeencombinedwithseveralMLtechniques
todealwithdifferentglobalandengineeringoptimizationproblems,alsoreal-world
applications. However, this book addresses the issues of two important computer
sciencesstrategies:MHsandML.Theideaofcombiningthetechniquesistoimprove
theperformanceoftheoriginalmethodsindifferentapplications.
The book guides the reader along with different and exciting implementations,
butitalsoincludesthetheoreticalsupportthatpermitsunderstandingofalltheideas
presented in the chapter. Moreover, each chapter that offers applications includes
comparisonsandupdatedreferencesthatsupporttheresultsobtainedbytheproposed
approaches.Atthesametime,everychapterprovidesthereaderwithapracticalguide
to go to the reference sources. The book was designed for graduate and postgrad-
uate education, where students can find support for reinforcing or as the basis for
their consolidation; researchers can polish their knowledge. Also, professors can
findsupportfortheteachingprocessinareasinvolvingmachinevisionorasexam-
plesrelatedtomaintechniquesaddressed.Additionally,professionalswhowantto
learnandexploretheadvancesinconceptsandimplementationofoptimizationand
machine learning-based algorithms applied to several real-world applications can
findinthisbookanexcellentguideforsuchpurpose.
Thisexcitingbookhas30chaptersorganizedconsideringanoverviewofmeta-
heuristics(MHs)andmachinelearning(ML)methodsappliedtosolvevariousreal-
worldapplications.Inthissense,Chapters1and2providethecross-entropy-based
thresholding segmentation of magnetic resonance prostatic images and hyperpa-
rameter optimization in a convolutional neural network using metaheuristic algo-
rithms, respectively. Chapter 3 presents a diagnosis of collateral effects in climate
changethroughtheidentificationofleafdamageusinganovelheuristicsandmachine
learningframework.Chapter4explainsthefeatureengineeringformachinelearning
anddeeplearning-assistedwirelesscommunication.Chapter5introducesthegenetic
vii
viii Introduction
operators and their impact on the training of deep neural networks. In Chapter 6,
theimplementationofmetaheuristicswithextremelearningmachinesisdescribed.
Chapter 7 presents the architecture optimization of convolutional neural networks
bymicrogeneticalgorithms.InChapter8,optimizingconnectionweightsinneural
networksusingamemeticalgorithmincorporatingchaostheoryisintroduced.
Further, Chapter 9 provides a review of metaheuristic optimization algorithms
for wireless sensor networks. In Chapter 10, a metaheuristic algorithm for white
blood cell classification in healthcare informatics is presented. In Chapter 11, a
review of multi-level thresholding image segmentation using nature-inspired opti-
mization algorithms is introduced. Chapter 12 explains the hybrid Harris hawks
optimization with differential evolution for data clustering. Chapter 13 introduces
the variable mesh optimization for continuous optimization and multimodal prob-
lems.Chapter14providestrafficcontrolusingimageprocessinganddeeplearning
techniques. Chapter 15 introduces the drug design and discovery: theory, applica-
tions,openissues,andchallenges.ThethresholdingalgorithmappliedtochestX-ray
imageswithpneumoniaispresentedinChapter16.
Moreover, Chapter 17 presents a comprehensive review of artificial neural
networks on stock market prediction. Chapter 18 introduces the image classifica-
tionwithconvolutionalneuralnetworks.Chapter19providestheappliedmachine
learningtechniquestofindpatternsandtrendsinbicycle-sharingsystemsinfluenced
bytrafficaccidentsandviolenteventsinGuadalajara,Mexico.InChapter20,areview
onmachinereadingcomprehension(LSTM)ispresented.Chapter21introducesa
survey of metaheuristic algorithms for solving optimization problems. Chapter 22
integratesmetaheuristicalgorithmsandminimumcross-entropyforimagesegmen-
tation in mist conditions. Chapter 23 provides a machine learning application for
particlephysics:Mexico’sinvolvementintheHyper-Kamiokandeobservatory.
Besides,Chapter24providesanovelmetaheuristicapproachforimagecontrast
enhancementbasedongrayscalemapping.Chapter25presentsthegeospatialdata
mining technique survey. In Chapter 26, an integration of Internet of things and
cloudcomputingforcardiachealthrecognitionisdiscussed.Chapter27introduces
the combinatorial optimization for artificial intelligence-enabled mobile network
automation.Chapter28presentstheperformance optimizationofaPIDcontroller
basedonparameterestimationusingmetaheuristictechniques.Chapter29provides
thesolarirradiationchangedetectionforphotovoltaicsystemsthroughANNtrained
withametaheuristicalgorithm.Finally,inChapter30,thegeneticalgorithm-based
globalandlocalfeatureselectionapproachforhandwrittennumeralrecognitionis
presented.
Itisimportanttomentionthatanadvantageofthisstructureisthateachchapter
couldbereadseparately.Thisbookisanimportantreferenceforhybridizedmeta-
heuristics(MHs)withmachinelearning(ML)methodsforvariousreal-worldappli-
cations. These areas are relevant and are in constant evolution. For that reason, it
Introduction ix
ishardtocollectalltheinformationinasinglebook.Icongratulatetheauthorsfor
theireffortanddedicationtoassemblingthetopicsaddressedinthebook.
DiegoOliva
EssamH.Houssein
SalvadorHinojosa
