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Genetic Programming Theory and Practice XVII

Overview of attention for book
Cover of 'Genetic Programming Theory and Practice XVII'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Characterizing the Effects of Random Subsampling on Lexicase Selection
  3. Altmetric Badge
    Chapter 2 It Is Time for New Perspectives on How to Fight Bloat in GP
  4. Altmetric Badge
    Chapter 3 Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives
  5. Altmetric Badge
    Chapter 4 Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?
  6. Altmetric Badge
    Chapter 5 Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
  7. Altmetric Badge
    Chapter 6 Temporal Memory Sharing in Visual Reinforcement Learning
  8. Altmetric Badge
    Chapter 7 The Evolution of Representations in Genetic Programming Trees
  9. Altmetric Badge
    Chapter 8 How Competitive Is Genetic Programming in Business Data Science Applications?
  10. Altmetric Badge
    Chapter 9 Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming
  11. Altmetric Badge
    Chapter 10 Evolutionary Computation and AI Safety
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    Chapter 11 Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?
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    Chapter 12 Hands-on Artificial Evolution Through Brain Programming
  14. Altmetric Badge
    Chapter 13 Comparison of Linear Genome Representations for Software Synthesis
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    Chapter 14 Enhanced Optimization with Composite Objectives and Novelty Pulsation
  16. Altmetric Badge
    Chapter 15 New Pathways in Coevolutionary Computation
  17. Altmetric Badge
    Chapter 16 2019 Evolutionary Algorithms Review
  18. Altmetric Badge
    Chapter 17 Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model
  19. Altmetric Badge
    Chapter 18 Modelling Genetic Programming as a Simple Sampling Algorithm
  20. Altmetric Badge
    Chapter 19 An Evolutionary System for Better Automatic Software Repair
Attention for Chapter 5: Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
6 Mendeley
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Chapter title
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Chapter number 5
Book title
Genetic Programming Theory and Practice XVII
Published in
arXiv, May 2020
DOI 10.1007/978-3-030-39958-0_5
Book ISBNs
978-3-03-039957-3, 978-3-03-039958-0
Authors

Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller, Kammerer, Lukas, Kronberger, Gabriel, Burlacu, Bogdan, Winkler, Stephan M., Kommenda, Michael, Affenzeller, Michael

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 17%
Student > Ph. D. Student 1 17%
Student > Postgraduate 1 17%
Unknown 3 50%
Readers by discipline Count As %
Arts and Humanities 1 17%
Chemical Engineering 1 17%
Biochemistry, Genetics and Molecular Biology 1 17%
Engineering 1 17%
Unknown 2 33%

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 30 September 2021.
All research outputs
#14,893,975
of 22,148,987 outputs
Outputs from arXiv
#355,598
of 897,505 outputs
Outputs of similar age
#188,776
of 300,413 outputs
Outputs of similar age from arXiv
#14,267
of 33,681 outputs
Altmetric has tracked 22,148,987 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 897,505 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 53% 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 300,413 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33,681 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.