Genetic Programming Theory and Practice XIV

· · ·
· Springer
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About this ebook

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.


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