03319nam 2200481 450 991073941740332120230526173557.0981-19-8460-310.1007/978-981-19-8460-0(MiAaPQ)EBC7212694(Au-PeEL)EBL7212694(CKB)26257613500041(DE-He213)978-981-19-8460-0(PPN)26909363X(EXLCZ)992625761350004120230526d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierGenetic Programming Theory and Practice XIX /Leonardo Trujillo [and three others], editorsFirst edition.Singapore :Springer Nature Singapore Pte Ltd,[2023]©20231 online resource (272 pages)Genetic and Evolutionary Computation SeriesPrint version: Trujillo, Leonardo Genetic Programming Theory and Practice XIX Singapore : Springer,c2023 9789811984594 Includes bibliographical references and index.Chapter 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.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 series.Genetic programming (Computer science)Genetic programming (Computer science)006.31Trujillo LeonardoMiAaPQMiAaPQMiAaPQBOOK9910739417403321Genetic Programming Theory and Practice XIX3552434UNINA