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020			_a9789819938148 _9978-981-99-3814-8
024	7		_a10.1007/978-981-99-3814-8 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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245	1	0	_aHandbook of Evolutionary Machine Learning _h[electronic resource] / _cedited by Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang.
250			_a1st ed. 2024.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024.
300			_aXVI, 768 p. 202 illus., 148 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		_aGenetic and Evolutionary Computation, _x1932-0175
505	0		_aPart 1. Overview chapters -- Chapter 1. EML Fundamentals -- Chapter 2. EML in Supervised Learning -- Chapter 3. EML in Unsupervised Learning -- Chapter 4. EML in Reinforcement Learning -- Part 2. Evolutionary Computation as Machine Learning -- Chapter 5. Evolutionary Clustering -- Chapter 6. Evolutionary Classification and Regression -- Chapter 7. Evolutionary Ensemble Learning -- Chapter 8. Evolutionary Deep Learning -- Chapter 9. Evolutionary Generative Models -- Part 3. Evolutionary Computation for Machine Learning -- Chapter 10. Evolutionary Data Preparation -- Chapter 11. Evolutionary Feature Engineering and Selection -- Chapter 12. Evolutionary Model Parametrization -- Chapter 13. Evolutionary Model Design -- Chapter 14. Evolutionary Model Validation -- Part 4. Applications -- Chapter 15. EML in Medicine -- Chapter 16. EML in Robotics -- Chapter 17. EML in Finance -- Chapter 18. EML in Science -- Chapter 19. EML in Environmental Science -- Chapter 20. EML in the Arts.
520			_aThis book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.
650		0	_aArtificial intelligence. _93407
650		0	_aMachine learning. _91831
650		0	_aComputational intelligence. _97716
650		0	_aEvolution (Biology). _916952
650	1	4	_aArtificial Intelligence. _93407
650	2	4	_aMachine Learning. _91831
650	2	4	_aComputational Intelligence. _97716
650	2	4	_aEvolutionary Biology. _934708
700	1		_aBanzhaf, Wolfgang. _eeditor. _0(orcid) _10000-0002-6382-3245 _4edt _4http://id.loc.gov/vocabulary/relators/edt _993508
700	1		_aMachado, Penousal. _eeditor. _0(orcid) _10000-0002-6308-6484 _4edt _4http://id.loc.gov/vocabulary/relators/edt _993509
700	1		_aZhang, Mengjie. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _993511
710	2		_aSpringerLink (Online service) _993512
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819938131
776	0	8	_iPrinted edition: _z9789819938155
776	0	8	_iPrinted edition: _z9789819938162
830		0	_aGenetic and Evolutionary Computation, _x1932-0175 _993513
856	4	0	_uhttps://doi.org/10.1007/978-981-99-3814-8
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