Genetic Programming Theory and Practice XIV / Genetic and Evolutionary Computation (PDF)
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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.
- 2018, 1st ed. 2018, 227 Seiten, Englisch
- Herausgegeben: Rick Riolo, Bill Worzel, Brian Goldman, Bill Tozier
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319970887
- ISBN-13: 9783319970882
- Erscheinungsdatum: 24.10.2018
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- Dateiformat: PDF
- Grösse: 6.68 MB
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