Result: Using physiological signals to evolve art
Title:
Using physiological signals to evolve art
Source:
Applications of evolutionary computing (EvoWorkshops 2006)Lecture notes in computer science. :633-641
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 11 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Bioinformatics, Bioinformatique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse statistique, Statistical analysis, Análisis estadístico, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Cerveau, Brain, Cerebro, Emotion émotivité, Emotion emotionality, Emoción emotividad, Encéphale, Encephalon, Encéfalo, Homme, Human, Hombre, Intérêt, Interest, Interés, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Méthode directe, Direct method, Método directo, Système nerveux central, Central nervous system, Sistema nervioso central, Théorie euclidienne, Euclidean theory, Teoría euclidiana
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Graduate School of Advanced Imaging Science, Multimedia and Film, Department of Image Engineering, Chung-Ang University, Seoul 156-756, Korea, Republic of
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19131298
Database:
PASCAL Archive
Further Information
Human subjectivity have always posed a problem when it comes to judging designs. The line that divides what is interesting or not is blurred by the different interpretations as varied as the individuals themselves. Some approaches have made use of novelty in determining interestingness. However, computational measures of novelty such as the Euclidean distance are mere approximations to what the human brain finds interesting. In this paper, we explore the possibility of determining interestingness in a more direct method by using learning techniques such as Support Vector Machines to identify emotions from physiological signals, and then use genetic algorithms to evolve artworks that resulted in positive emotional signals.