Genetic Programming Theory and Practice XIV
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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 RegressionHybrid Structural and Behavioral Diversity Methods in GPMulti-Population Competitive Coevolution for Anticipation of Tax EvasionEvolving Artificial General Intelligence for Video Game ControllersA Detailed Analysis of...
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.
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.
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