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008			230311s2023 si | o |||| 0|eng d
020			_a9789811984600
024	7		_a10.1007/978-981-19-8460-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA75.5 _b2023 EB
245	0	0	_aGenetic Programming Theory and Practice XIX _cedited by Leonardo Trujillo, Stephan M Winkler, Sara Silva, Wolfgang Banzhaf
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			_atext file _bPDF _2rda
490	0		_aGenetic and Evolutionary Computation _x1932-0175
505	0		_aChapter 1. Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data -- Chapter 2. Correlation versus RMSE Loss Functions in Symbolic Regression Tasks -- Chapter 3. GUI-Based, Efficient Genetic Programming and AI Planning For Unity3D -- Chapter 4. Genetic Programming for Interpretable and Explainable Machine Learning -- Chapter 5. Biological Strategies ParetoGP Enables Analysis of Wide and Ill-Conditioned Data from Nonlinear Systems -- Chapter 6. GP-Based Generative Adversarial Models -- Chapter 7. Modelling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification through Inferential Knowledge -- Chapter 8. Life as a Cyber-Bio-Physical System -- Chapter 9. STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison -- Chapter 10. Evolving Complexity is Hard -- Chapter 11. ESSAY: Computers Are Useless ... They Only Give Us Answers.
520			_aThis book brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year´s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the art in GP research.
988			_aSpringer_Computer_2023
650		7	_2embne _9139268 _aInformática
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-8460-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _ek _zSI