Buch, Englisch, 279 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 452 g
Buch, Englisch, 279 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 452 g
Reihe: Genetic and Evolutionary Computation
ISBN: 978-1-4419-4547-1
Verlag: Springer US
Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan’s Center for the Study of Complex Systems. It aims to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
- Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
Weitere Infos & Material
Genetic Programming: Theory and Practice.- Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming.- Manipulation of Convergence in Evolutionary Systems.- Large-Scale, Time-Constrained Symbolic Regression-Classification.- Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction.- Towards an Information Theoretic Framework for Genetic Programming.- Investigating Problem Hardness of Real Life Applications.- Improving the Scalability of Generative Representations for Openended Design.- Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming.- Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design.- Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs.- Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models.- Improving Performance and Cooperation in Multi-Agent Systems.- An Empirical Study of Multi-Objective Algorithms for Stock Ranking.- Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center.
