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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 Genetic Programming
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    Chapter 2 Controlling Bloat through Parsimonious Elitist Replacement and Spatial Structure
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    Chapter 3 Generation of VNS Components with Grammatical Evolution for Vehicle Routing
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    Chapter 4 Understanding Expansion Order and Phenotypic Connectivity in πGE
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    Chapter 5 PhenoGP: Combining Programs to Avoid Code Disruption
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    Chapter 6 Reducing Wasted Evaluations in Cartesian Genetic Programming
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    Chapter 7 Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data
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    Chapter 8 Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming
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    Chapter 9 Robustness and Evolvability of Recombination in Linear Genetic Programming
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    Chapter 10 On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology
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    Chapter 11 Discovering Subgroups by Means of Genetic Programming
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    Chapter 12 Genetic Programming
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    Chapter 13 Searching for Novel Classifiers
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    Chapter 14 Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming
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    Chapter 15 Automated Problem Decomposition for the Boolean Domain with Genetic Programming
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    Chapter 16 A Multi-objective Optimization Energy Approach to Predict the Ligand Conformation in a Docking Process
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    Chapter 17 Semantic Bias in Program Coevolution
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    Chapter 18 A New Implementation of Geometric Semantic GP and Its Application to Problems in Pharmacokinetics
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    Chapter 19 A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification
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    Chapter 20 Global Top-Scoring Pair Decision Tree for Gene Expression Data Analysis
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    Chapter 21 Asynchronous Evaluation Based Genetic Programming: Comparison of Asynchronous and Synchronous Evaluation and Its Analysis
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    Chapter 22 Genetic Programming
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    Chapter 23 Examining the Diversity Property of Semantic Similarity Based Crossover
Attention for Chapter 9: Robustness and Evolvability of Recombination in Linear Genetic Programming
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Chapter title
Robustness and Evolvability of Recombination in Linear Genetic Programming
Chapter number 9
Book title
Genetic Programming
Published by
Springer Berlin Heidelberg, January 2013
DOI 10.1007/978-3-642-37207-0_9
Book ISBNs
978-3-64-237206-3, 978-3-64-237207-0
Authors

Ting Hu, Wolfgang Banzhaf, Jason H. Moore

Editors

Krzysztof Krawiec, Alberto Moraglio, Ting Hu, A. Şima Etaner-Uyar, Bin Hu

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 6%
Unknown 17 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 28%
Student > Master 3 17%
Student > Bachelor 2 11%
Researcher 2 11%
Professor 1 6%
Other 2 11%
Unknown 3 17%
Readers by discipline Count As %
Computer Science 10 56%
Agricultural and Biological Sciences 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Chemistry 1 6%
Engineering 1 6%
Other 0 0%
Unknown 4 22%