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020			_a9789819938148
024	7		_a10.1007/978-981-99-3814-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050	0	4	_aQ334-342 _b2024
050	0	4	_aTA347.A78 _b2024
245	0	0	_aHandbook of Evolutionary Machine Learning _cedited by Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang
250			_afirst edition 2024
264		1	_aSingapore _c2024 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_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.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-3814-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
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988			_aSpringer_Computer_2024
999			_c397722 _d397722