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.