Genetic Programming Theory and Practice XIV (Genetic and Evolutionary Computation)

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.