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Genetic Programming Theory and Practice XIV

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: S...

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Corporate Author: SpringerLink (Online service)
Other Authors: Riolo, Rick. (Editor, http://id.loc.gov/vocabulary/relators/edt), Worzel, Bill. (Editor, http://id.loc.gov/vocabulary/relators/edt), Goldman, Brian. (Editor, http://id.loc.gov/vocabulary/relators/edt), Tozier, Bill. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
Edition:1st ed. 2018.
Series:Genetic and Evolutionary Computation,
Subjects:
Artificial intelligence.
Computational intelligence.
Algorithms.
Artificial Intelligence.
Computational Intelligence.
Algorithm Analysis and Problem Complexity.
Online Access:https://doi.org/10.1007/978-3-319-97088-2
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.
Physical Description:XV, 227 p. 52 illus. online resource.
ISBN:9783319970882
ISSN:1932-0167

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