Genetic Programming Theory and Practice XIV -

Genetic Programming Theory and Practice XIV

Buch | Hardcover
XV, 227 Seiten
2018 | 1st ed. 2018
Springer International Publishing (Verlag)
978-3-319-97087-5 (ISBN)
53,49 inkl. MwSt

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.


1 Similarity-based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression.- 2 An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming.- 3 Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion.- 4 Evolving Artificial General Intelligence for Video Game Controllers.- 5 A Detailed Analysis of a PushGP Run.- 6 Linear Genomes for Structured Programs.- 7 Neutrality, Robustness, and Evolvability in Genetic Programming.- 8 Local Search is Underused in Genetic Programming.- 9 PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification.- 10 Discovering Relational Structural in Program Synthesis Problems with Analogical Reasoning.- 11 An Evolutionary Algorithm for Big Data Multi-Class Classification Problems.- 12 A Genetic Framework for Building Dispersion Operators in the Semantic Space.- 13 Assisting Asset Model Development with Evolutionary Augmentation.- 14 Identifying andHarnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool.

"This highly technical book is meant for a very specialized audience: researchers in GP. The topics discussed offer interesting insight into how research in GP is evolving. ... I strongly recommend this book for researchers in evolutionary computing and GP." (S. V. Nagaraj, Computing Reviews, November 12, 2020)

Erscheinungsdatum
Reihe/Serie Genetic and Evolutionary Computation
Zusatzinfo XV, 227 p. 52 illus.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 523 g
Themenwelt Informatik Theorie / Studium Algorithmen
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Algorithm analysis and problem complexity • Analogical Reasoning • Artificial Evolution • artificial general intelligence • Dispersion Operators • Distributed Probabilistic Rule • Evolutionary Augmentation • Evolution of Models • Feature Selection • genetic programming • Genetic Programming Applications • Genetic Programming Theory • program induction • Symbolic Regression
ISBN-10 3-319-97087-9 / 3319970879
ISBN-13 978-3-319-97087-5 / 9783319970875
Zustand Neuware
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