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<p></p><p>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:&nbsp;</p>

<p></p><ul><li>Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression<br></li><li>Hybrid Structural and Behavioral Diversity Methods in GP<br></li><li>Multi-Population Competitive Coevolution for Anticipation of Tax Evasion<br></li><li>Evolving Artificial General Intelligence for Video Game Controllers<br></li><li>A Detailed Analysis of a PushGP Run<br></li><li>Linear Genomes for Structured Programs<br></li><li>Neutrality, Robustness, and Evolvability in GP<br></li><li>Local Search in GP<br></li><li>PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification<br></li><li>Relational Structure in Program Synthesis Problems with Analogical Reasoning<br></li><li>An Evolutionary Algorithm for Big Data Multi-Class Classification Problems<br></li><li>A Generic Framework for Building Dispersion Operators in the Semantic Space<br></li><li>Assisting Asset Model Development with Evolutionary Augmentation<br></li><li>Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool&nbsp;</li></ul> <p>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.</p><br><p></p>