Genetic Programming Theory and Practice XIV

Rick Riolo

ISBN 10: 3030073009 / ISBN 13: 9783030073008

Editore: Springer International Publishing, Springer International Publishing, 2019

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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 RegressionHybrid Structural and Behavioral Diversity Methods in GPMulti-Population Competitive Coevolution for Anticipation of Tax EvasionEvolving Artificial General Intelligence for Video Game ControllersA Detailed Analysis of a PushGP RunLinear Genomes for Structured ProgramsNeutrality, Robustness, and Evolvability in GPLocal Search in GPPRETSL: Distributed Probabilistic Rule Evolution for Time-Series ClassificationRelational Structure in Program Synthesis Problems with Analogical ReasoningAn Evolutionary Algorithm for Big Data Multi-Class Classification ProblemsA Generic Framework for Building Dispersion Operators in the Semantic SpaceAssisting Asset Model Development with Evolutionary AugmentationBuilding 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.

Codice articolo 9783030073008

Dati bibliografici

• Titolo: Genetic Programming Theory and Practice XIV
• Editore: Springer International Publishing, Springer International Publishing
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030073009
• ISBN 13: 9783030073008
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

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.

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.