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082 0 4 _a004.0151
_223
245 1 0 _aGenetic Programming Theory and Practice XIX
_h[electronic resource] /
_cedited by Leonardo Trujillo, Stephan M. Winkler, Sara Silva, Wolfgang Banzhaf.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXIV, 262 p. 104 illus., 93 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aGenetic and Evolutionary Computation,
_x1932-0175
500 _aAcceso multiusuario
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.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputer science.
650 0 _aBionics.
650 0 _aAlgorithms.
650 1 4 _aModels of Computation.
650 2 4 _aBioinspired Technologies.
650 2 4 _aAlgorithms.
700 1 _aTrujillo, Leonardo.
_eeditor.
_0(orcid)0000-0003-1812-5736
_1https://orcid.org/0000-0003-1812-5736
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700 1 _aWinkler, Stephan M.
_eeditor.
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700 1 _aSilva, Sara.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBanzhaf, Wolfgang.
_eeditor.
_0(orcid)0000-0002-6382-3245
_1https://orcid.org/0000-0002-6382-3245
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811984594
776 0 8 _iPrinted edition:
_z9789811984617
776 0 8 _iPrinted edition:
_z9789811984624
830 0 _aGenetic and Evolutionary Computation,
_x1932-0175
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-19-8460-0
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