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.
