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Genetic Programming

Overview of attention for book
Cover of 'Genetic Programming'

Table of Contents

  1. Altmetric Badge
    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
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
9 X users
facebook
2 Facebook pages

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
2 Mendeley
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Title
Genetic Programming
Published by
arXiv, March 2018
DOI 10.1007/978-3-319-77553-1
ISBNs
978-3-31-977552-4, 978-3-31-977553-1
Authors

Andrew Lensen, Bing Xue, Mengjie Zhang

Editors

Castelli, Mauro, Sekanina, Lukas, Zhang, Mengjie, Cagnoni, Stefano, García-Sánchez, Pablo

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 150%
Researcher 3 150%
Student > Master 3 150%
Readers by discipline Count As %
Computer Science 5 250%
Energy 1 50%
Engineering 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 04 January 2022.
All research outputs
#6,727,526
of 24,002,307 outputs
Outputs from arXiv
#142,915
of 1,011,770 outputs
Outputs of similar age
#114,465
of 334,826 outputs
Outputs of similar age from arXiv
#3,404
of 22,047 outputs
Altmetric has tracked 24,002,307 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,011,770 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,826 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 22,047 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.