Genetic Programming Theory and Practice XIV - Brossura

Brossura

ISBN 10: 3030073009 ISBN 13: 9783030073008

Casa editrice: Springer, 2019

Riassunto

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.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030073009
• ISBN 13: 9783030073008
• Rilegatura: Copertina flessibile
• Numero di pagine: 244
• Redattore: Riolo Rick, Worzel Bill, Goldman Brian, Tozier Bill
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania