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Genetic Programming

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Cover of 'Genetic Programming'

Table of Contents

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    Book Overview
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    Chapter 1 Hessian Complexity Measure for Genetic Programming-Based Imputation Predictor Selection in Symbolic Regression with Incomplete Data
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    Chapter 2 Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing
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    Chapter 3 Incremental Evolution and Development of Deep Artificial Neural Networks
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    Chapter 4 Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
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    Chapter 5 Comparing Genetic Programming Approaches for Non-functional Genetic Improvement
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    Chapter 6 Automatically Evolving Lookup Tables for Function Approximation
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    Chapter 7 Optimising Optimisers with Push GP
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    Chapter 8 An Evolutionary View on Reversible Shift-Invariant Transformations
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    Chapter 9 Benchmarking Manifold Learning Methods on a Large Collection of Datasets
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    Chapter 10 Ensemble Genetic Programming
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    Chapter 11 SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
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    Chapter 12 Effect of Parent Selection Methods on Modularity
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    Chapter 13 Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models
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    Chapter 14 Challenges of Program Synthesis with Grammatical Evolution
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    Chapter 15 Detection of Frailty Using Genetic Programming
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    Chapter 16 Is k Nearest Neighbours Regression Better Than GP?
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    Chapter 17 Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling
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    Chapter 18 Classification of Autism Genes Using Network Science and Linear Genetic Programming
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Title
Genetic Programming
Published by
Springer International Publishing, May 2020
DOI 10.1007/978-3-030-44094-7
ISBNs
978-3-03-044093-0, 978-3-03-044094-7
Editors

Hu, Ting, Lourenço, Nuno, Medvet, Eric, Divina, Federico

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