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008			230714s2023 si | o |||| 0|eng d
020			_a9789819918546
024	7		_a10.1007/978-981-99-1854-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA347 .E96 _b2023 EB
100	1		_aJin, Yaochu _d1966- _eautor _4http://id.loc.gov/vocabulary/relators/aut _9681795
245	1	0	_aComputational Evolution of Neural and Morphological Development : _bTowards Evolutionary Developmental Artificial Intelligence _cby Yaochu Jin
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aNatural Computing Series _x2627-6461
505	0		_aComputational Models of Evolution and Development -- Analysis of Gene Regulatory Networks -- Evolutionary Synthesis of Gene Regulatory Dynamics -- Evolution of Morphological Development -- Evolution of Neural Development -- Computational Brain-Body Co-Evolution -- Evolutionary Morphogenetic Self-Organization of Swarm Robots -- Towards Evolutionary Developmental Systems.
520			_aThis book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the author's extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence.
988			_aSpringer_Computer_2023
650		7	_2embne _9667195 _aComputación evolutiva
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-1854-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI