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001 978-3-030-72069-8
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020 _a9783030720698
_9978-3-030-72069-8
024 7 _a10.1007/978-3-030-72069-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aAutomated Design of Machine Learning and Search Algorithms
_h[electronic resource] /
_cedited by Nelishia Pillay, Rong Qu.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVIII, 187 p. 42 illus., 28 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNatural Computing Series,
_x2627-6461
505 0 _aChapter 1: Recent Developments of Automated Machine Learning and Search Techniques -- Chapter 2: Automated Machine Learning -- Chapter 3: A General Model for Automated Algorithm Design -- Chapter 4: Rigorous Performance Analysis of Hyper-Heuristics -- Chapter 5: AutoMoDe -- Chapter 6: A cross-domain method for generation of constructive and perturbative heuristics -- Chapter 7: Hyper-heuristics -- Chapter 8: Towards Real-time Federated Evolutionary Neural -- Chapter 9: Knowledge Transfer in Genetic Programming -- Chapter 10: Automated Design of Classification Algorithms -- Chapter 11: Automated Design (AutoDes).
520 _aThis book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field. The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
650 0 _aArtificial intelligence.
650 1 4 _aArtificial Intelligence.
700 1 _aPillay, Nelishia.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aQu, Rong.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030720681
776 0 8 _iPrinted edition:
_z9783030720704
776 0 8 _iPrinted edition:
_z9783030720711
830 0 _aNatural Computing Series,
_x2627-6461
856 4 0 _uhttps://doi.org/10.1007/978-3-030-72069-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c176826
_d176826