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Evolutionary and Biologically Inspired Music, Sound, Art and Design

Overview of attention for book
Cover of 'Evolutionary and Biologically Inspired Music, Sound, Art and Design'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Aesthetics, Art, Evolution
  3. Altmetric Badge
    Chapter 2 Application of an Island Model Genetic Algorithm for a Multi-track Music Segmentation Problem
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    Chapter 3 evoDrummer: Deriving Rhythmic Patterns through Interactive Genetic Algorithms
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    Chapter 4 Darwinian Pianos: Realtime Composition Based on Competitive Evolutionary Process
  6. Altmetric Badge
    Chapter 5 Finding image features associated with high aesthetic value by machine learning
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    Chapter 6 Aesthetic Measures for Evolutionary Vase Design
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    Chapter 7 Inverse Mapping with Sensitivity Analysis for Partial Selection in Interactive Evolution
  9. Altmetric Badge
    Chapter 8 Swarmic Sketches and Attention Mechanism
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    Chapter 9 Swarmic Paintings and Colour Attention
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    Chapter 10 Evolving Glitch Art
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    Chapter 11 EvoSpace-Interactive: A Framework to Develop Distributed Collaborative-Interactive Evolutionary Algorithms for Artistic Design
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    Chapter 12 Feature Selection and Novelty in Computational Aesthetics
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    Chapter 13 Biologically–Inspired Motion Pattern Design of Multi–legged Creatures
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    Chapter 14 Decision Chain Encoding: Evolutionary Design Optimization with Complex Constraints
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    Chapter 15 Story Characterization Using Interactive Evolution in a Multi-Agent System
  17. Altmetric Badge
    Chapter 16 Sentient World: Human-Based Procedural Cartography
Attention for Chapter 5: Finding image features associated with high aesthetic value by machine learning
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Mentioned by

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1 X user

Citations

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1 Dimensions

Readers on

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20 Mendeley
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Chapter title
Finding image features associated with high aesthetic value by machine learning
Chapter number 5
Book title
Evolutionary and Biologically Inspired Music, Sound, Art and Design
Published in
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-36955-1_5
Book ISBNs
978-3-64-236954-4, 978-3-64-236955-1
Authors

Penousal Machado, James McDermott, Adrian Carballal, Vic Ciesielski, Perry Barile, Karen Trist

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 30%
Student > Ph. D. Student 4 20%
Student > Postgraduate 2 10%
Student > Bachelor 1 5%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 4 20%
Readers by discipline Count As %
Computer Science 12 60%
Psychology 2 10%
Engineering 1 5%
Design 1 5%
Unknown 4 20%
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 04 April 2013.
All research outputs
#15,267,294
of 22,703,044 outputs
Outputs from Lecture notes in computer science
#4,645
of 8,124 outputs
Outputs of similar age
#181,474
of 280,707 outputs
Outputs of similar age from Lecture notes in computer science
#192
of 314 outputs
Altmetric has tracked 22,703,044 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 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 280,707 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 314 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.