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:

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  Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression

  
  


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  Hybrid Structural and Behavioral Diversity Methods in GP

  
  


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  Multi-Population Competitive Coevolution for Anticipation of Tax Evasion

  
  


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  Evolving Artificial General Intelligence for Video Game Controllers

  
  


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  A Detailed Analysis of a PushGP Run

  
  


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  Linear Genomes for Structured Programs

  
  


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  Neutrality, Robustness, and Evolvability in GP

  
  


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  Local Search in GP

  
  


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  PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification

  
  


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  Relational Structure in Program Synthesis Problems with Analogical Reasoning

  
  


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  An Evolutionary Algorithm for Big Data Multi-Class Classification Problems

  
  


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  A Generic Framework for Building Dispersion Operators in the Semantic Space

  
  


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  Assisting Asset Model Development with Evolutionary Augmentation

  
  


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  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.