Metaheuristics in ML PDF
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Studies 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 SeriesEditor JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland The series “Studies in Computational Intelligence” (SCI) publishes new develop- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand withahighquality.Theintentistocoverthetheory,applications,anddesignmethods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms,evolutionarycomputation,artificialintelligence,cellularautomata,self- organizingsystems,softcomputing,fuzzysystems,andhybridintelligentsystems. Ofparticularvaluetoboththecontributorsandthereadershiparetheshortpublica- tiontimeframeandtheworld-widedistribution,whichenablebothwideandrapid disseminationofresearchoutput. IndexedbySCOPUS,DBLP,WTIFrankfurteG,zbMATH,SCImago. AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience. Moreinformationaboutthisseriesathttp://www.springer.com/series/7092 · · 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 ISBN978-3-030-70541-1 ISBN978-3-030-70542-8 (eBook) https://doi.org/10.1007/978-3-030-70542-8 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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
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