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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.
List of contents
1 Similarity-based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression.- 2 An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming.- 3 Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion.- 4 Evolving Artificial General Intelligence for Video Game Controllers.- 5 A Detailed Analysis of a PushGP Run.- 6 Linear Genomes for Structured Programs.- 7 Neutrality, Robustness, and Evolvability in Genetic Programming.- 8 Local Search is Underused in Genetic Programming.- 9 PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification.- 10 Discovering Relational Structural in Program Synthesis Problems with Analogical Reasoning.- 11 An Evolutionary Algorithm for Big Data Multi-Class Classification Problems.- 12 A Genetic Framework for Building Dispersion Operators in the Semantic Space.- 13 Assisting Asset Model Development with Evolutionary Augmentation.- 14 Identifying andHarnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool.
Summary
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.
Additional text
“This highly technical book is meant for a very specialized audience: researchers in GP. The topics discussed offer interesting insight into how research in GP is evolving. … I strongly recommend this book for researchers in evolutionary computing and GP.” (S. V. Nagaraj, Computing Reviews, November 12, 2020)
Report
"This highly technical book is meant for a very specialized audience: researchers in GP. The topics discussed offer interesting insight into how research in GP is evolving. ... I strongly recommend this book for researchers in evolutionary computing and GP." (S. V. Nagaraj, Computing Reviews, November 12, 2020)
