LEADER 03319nam 2200481 450 001 9910739417403321 005 20230526173557.0 010 $a981-19-8460-3 024 7 $a10.1007/978-981-19-8460-0 035 $a(MiAaPQ)EBC7212694 035 $a(Au-PeEL)EBL7212694 035 $a(CKB)26257613500041 035 $a(DE-He213)978-981-19-8460-0 035 $a(PPN)26909363X 035 $a(EXLCZ)9926257613500041 100 $a20230526d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aGenetic Programming Theory and Practice XIX /$fLeonardo Trujillo [and three others], editors 205 $aFirst edition. 210 1$aSingapore :$cSpringer Nature Singapore Pte Ltd,$d[2023] 210 4$dİ2023 215 $a1 online resource (272 pages) 225 1 $aGenetic and Evolutionary Computation Series 311 08$aPrint version: Trujillo, Leonardo Genetic Programming Theory and Practice XIX Singapore : Springer,c2023 9789811984594 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis book brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year´s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the art in GP research. 410 0$aGenetic and evolutionary computation series. 606 $aGenetic programming (Computer science) 615 0$aGenetic programming (Computer science) 676 $a006.31 702 $aTrujillo$b Leonardo 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739417403321 996 $aGenetic Programming Theory and Practice XIX$93552434 997 $aUNINA