02283nam0 22004573i 450 VAN0024998720240806101421.776N978303048412520220912d2020 |0itac50 baengCH|||| |||||Topics in Uniform Approximation of Continuous FunctionsIleana Bucur, Gavriil PaltineanuChamBirkhäuserSpringer2020x, 140 p.ill.24 cm001VAN000513642001 Frontiers in mathematics210 Basel [etc.]Birkhäuser2004-VAN00249988Topics in Uniform Approximation of Continuous Functions290951041A10Approximation by polynomials [MSC 2020]VANC022984MF41A63Multidimensional problems [MSC 2020]VANC023022MF41A65Abstract approximation theory (approximation in normed linear spaces and other abstract spaces) [MSC 2020]VANC021234MF42A10Trigonometric approximation [MSC 2020]VANC022658MF46E10Topological linear spaces of continuous, differentiable or analytic functions [MSC 2020]VANC021695MF46E40Spaces of vector- and operator-valued functions [MSC 2020]VANC024769MFAlgebraic polynomialKW:KApproximationKW:KPolynomial functionsKW:KTrigonometric polynomialKW:KWeierstrassKW:KCHChamVANL001889BucurIleanaVANV2043381002775PaltineanuGavriilVANV2043391252227Birkhäuser <editore>VANV108193650Springer <editore>VANV108073650ITSOL20241115RICAhttp://doi.org/10.1007/978-3-030-48412-5E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN00249987BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08DLOAD e-book 4904 08eMF4904 20220912 Topics in Uniform Approximation of Continuous Functions2909510UNICAMPANIA04125nam 22005415 450 991098459310332120250228120745.09789819600779981960077410.1007/978-981-96-0077-9(CKB)37726495700041(MiAaPQ)EBC31927433(Au-PeEL)EBL31927433(DE-He213)978-981-96-0077-9(OCoLC)1503948826(EXLCZ)993772649570004120250228d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierGenetic Programming Theory and Practice XXI /edited by Stephan M. Winkler, Wolfgang Banzhaf, Ting Hu, Alexander Lalejini1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (535 pages)Genetic and Evolutionary Computation,1932-01759789819600762 9819600766 Chapter 1. Representation & Reachability: Assumption Impact in Data Modeling -- Chapter 2. EvoFeat: Genetic Programming-based Feature Engineering Approach to Tabular Data Classification -- Chapter 3. Deep Learning-Based Operators for Evolutionary Algorithms -- Chapter 4. Survey of Genetic Programming and Large Language Models -- Chapter 5. Evolving Many-Model Agents with Vector and Matrix Operations in Tangled Program Graphs -- Chapter 6. Automatic Design of Autoencoders using NeuroEvolution -- Chapter 7. Code Building Genetic Programming is Faster than PushGP -- Chapter 8. Sharpness-Aware Minimization in Genetic Programming -- Chapter 9. Tree-Based Grammatical Evolution with Non-Encoding Nodes -- Chapter 10. Genetic Programming with Memory for Approximate Data Reconstruction -- Chapter 11. Ratcheted Random Search for Self-Programming Boolean Networks -- Chapter 12. Exploring Non-Bloating Geometric Semantic Genetic Programming -- Chapter 13. Revisiting Gradient-based Local Search in Symbolic Regression -- Chapter 14. It’s Time to Revisit the Use of FPGAs for Genetic Programming -- Chapter 15. Interpretable Genetic Programming Models for Real-World Biomedical Images -- Chapter 16. Crafting Generative Art through Genetic Improvement: Managing Creative Outputs in Diverse Fitness Landscapes -- Chapter 17. Cell Regulation and the Early Evolution of Autonomous Control -- Chapter 18. How to Measure Explainability and Interpretability of Machine Learning Results -- Chapter 19. Lexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic Metrics -- Chapter 20. Using lineage age to augment search space exploration in lexicase selection.This 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.Genetic and Evolutionary Computation,1932-0175Artificial intelligenceArtificial IntelligenceArtificial intelligence.Artificial Intelligence.006.3Winkler Stephan M1768461Banzhaf Wolfgang61171Hu Ting1331606Lalejini Alexander1790208MiAaPQMiAaPQMiAaPQBOOK9910984593103321Genetic Programming Theory and Practice XXI4326292UNINA