Title: Genetic Programming Theory and Practice XIV (Genetic and Evolutionary Computation)
Publisher: Springer
Publication Date: 2018
Language: English
ISBN 10: 3319970879
ISBN 13: 9783319970875
Binding: Hardcover
Condition: New

About this title

Synopsis

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.

From the Back Cover

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.

"About this title" may belong to another edition of this title.

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