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Genetic Programming

Overview of attention for book
Cover of 'Genetic Programming'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Using GP Is NEAT: Evolving Compositional Pattern Production Functions
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    Chapter 2 Evolving the Topology of Large Scale Deep Neural Networks
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    Chapter 3 Evolving Graphs by Graph Programming
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    Chapter 4 Pruning Techniques for Mixed Ensembles of Genetic Programming Models
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    Chapter 5 Analyzing Feature Importance for Metabolomics Using Genetic Programming
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    Chapter 6 Generating Redundant Features with Unsupervised Multi-tree Genetic Programming
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    Chapter 7 On the Automatic Design of a Representation for Grammar-Based Genetic Programming
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    Chapter 8 Multi-level Grammar Genetic Programming for Scheduling in Heterogeneous Networks
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    Chapter 9 Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom
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    Chapter 10 Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States
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    Chapter 11 A Multiple Expression Alignment Framework for Genetic Programming
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    Chapter 12 Multi-objective Evolution of Ultra-Fast General-Purpose Hash Functions
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    Chapter 13 A Comparative Study on Crossover in Cartesian Genetic Programming
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    Chapter 14 Evolving Better RNAfold Structure Prediction
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    Chapter 15 Geometric Crossover in Syntactic Space
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    Chapter 16 Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling
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    Chapter 17 Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming
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    Chapter 18 Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-relational Association Rules in the Semantic Web
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    Chapter 19 Genetic Programming Hyper-Heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling
Attention for Chapter 6: Generating Redundant Features with Unsupervised Multi-tree Genetic Programming
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Chapter title
Generating Redundant Features with Unsupervised Multi-tree Genetic Programming
Chapter number 6
Book title
Genetic Programming
Published in
arXiv, April 2018
DOI 10.1007/978-3-319-77553-1_6
Book ISBNs
978-3-31-977552-4, 978-3-31-977553-1
Authors

Andrew Lensen, Bing Xue, Mengjie Zhang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Researcher 3 23%
Student > Master 3 23%
Unknown 4 31%
Readers by discipline Count As %
Computer Science 5 38%
Energy 1 8%
Engineering 1 8%
Unknown 6 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 March 2018.
All research outputs
#18,603,172
of 23,043,346 outputs
Outputs from arXiv
#539,124
of 946,967 outputs
Outputs of similar age
#255,669
of 329,129 outputs
Outputs of similar age from arXiv
#17,250
of 22,362 outputs
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So far Altmetric has tracked 946,967 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 22,362 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.