Genetic Programming Theory and Practice XIV

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Riolo, Rick (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Worzel, Bill (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Goldman, Brian (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Tozier, Bill (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Genetic and Evolutionary Computation,
• Θέματα: Artificial intelligence.; Computational intelligence.; Algorithms.; Artificial Intelligence.; Computational Intelligence.; Algorithm Analysis and Problem Complexity.
• Φυσική περιγραφή: XV, 227 p. 52 illus. online resource.
• ISBN: 9783319970882
• ISSN: 1932-0167
• DOI: 10.1007/978-3-319-97088-2

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression Hybrid Structural and Behavioral Diversity Methods in GP Multi-Population Competitive Coevolution for Anticipation of Tax Evasion Evolving Artificial General Intelligence for Video Game Controllers A Detailed Analysis of a PushGP Run Linear Genomes for Structured Programs Neutrality, Robustness, and Evolvability in GP Local Search in GP PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification Relational Structure in Program Synthesis Problems with Analogical Reasoning An Evolutionary Algorithm for Big Data Multi-Class Classification Problems A Generic Framework for Building Dispersion Operators in the Semantic Space Assisting Asset Model Development with Evolutionary Augmentation Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.