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.