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Evolutionary and Biologically Inspired Music, Sound, Art and Design

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Cover of 'Evolutionary and Biologically Inspired Music, Sound, Art and Design'

Table of Contents

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    Book Overview
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    Chapter 1 Aesthetics, Art, Evolution
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    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
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    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
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    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
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    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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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

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 32%
Student > Ph. D. Student 4 21%
Student > Postgraduate 2 11%
Student > Bachelor 1 5%
Student > Doctoral Student 1 5%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Computer Science 12 63%
Psychology 2 11%
Engineering 1 5%
Design 1 5%
Unknown 3 16%