3rd International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
April 2014, Granada, Spain
Part of evo* 2014
New this year: Special track on Artificial Neural Network applied to Music, Sound, Art and Design
LEONARDO Special Section
Authors of selected papers will be invited to submit expanded versions of their work for a planned special section on Evolutionary Art of the MIT Press journal "Leonardo".
Following the success of previous events and the importance of the field of evolutionary and biologically inspired (artificial neural network, swarm, alife) music, sound, art and design, evomusart has become an evo* conference with independent proceedings since 2012. Thus, evomusart 2014 is the twelfth European Event and the third International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design.
The use of biologically inspired techniques for the development of artistic systems is a recent, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.
The main goal of evomusart 2014 is to bring together researchers who are using biologically inspired computer techniques for artistic tasks, providing the opportunity to promote, present and discuss ongoing work in the area.
The event will be held in April, 2014 in Granada, Spain, as
part of the evo* event.
Submissions will be rigorously reviewed for scientific and artistic merit.
Accepted papers will be presented orally or as posters at the event and
included in the evomusart proceedings, published by Springer Verlag in a
dedicated volume of the Lecture Notes in Computer Science series. The acceptance
rate at evomusart 2013 was 30.5\% for papers
accepted for oral presentation, or 44.4% for oral and poster presentation combined. The evomusart 2013 submissions received on average 3.4 reviews each.
New this year: submitters are strongly encouraged to provide in all papers a link for download of media demonstrating their results, whether music, images, video, or other media types. Links should be anonymised for double-blind review, e.g. using a URL shortening service.
Topics of interest
Submissions should concern the use of biologically inspired computer techniques -- e.g. Evolutionary Computation, Artificial Life, Artificial Neural Networks, Swarm Intelligence, other artificial intelligence techniques -- in the generation, analysis and interpretation of art, music, design, architecture and other artistic fields. Topics of interest include, but are not limited to:
- Biologically Inspired Design and Art -- Systems that create drawings, images, animations, sculptures, poetry, text, designs, webpages, buildings, etc.;
- Biologically Inspired Sound and Music -- Systems that create musical pieces, sounds, instruments, voices, sound effects, sound analysis, etc.;
- Robotic-Based Evolutionary Art and Music;
- Other related artificial intelligence or generative techniques in the fields of Computer Music, Computer Art, etc.;
- Computational Aesthetics, Experimental Aesthetics; Emotional Response, Surprise, Novelty;
- Representation techniques;
- Surveys of the current state-of-the-art in the area; identification of weaknesses and strengths; comparative analysis and classification;
- Validation methodologies;
- Studies on the applicability of these techniques to related areas;
- New models designed to promote the creative potential of biologically inspired computation;
Computer Aided Creativity and computational creativity
- Systems in which biologically inspired computation is used to promote the creativity of a human user;
- New ways of integrating the user in the evolutionary cycle;
- Analysis and evaluation of: the artistic potential of biologically inspired art and music; the artistic processes inherent to these approaches; the resulting artefacts;
- Collaborative distributed artificial art environments;
- Techniques for automatic fitness assignment
- Systems in which an analysis or interpretation of the artworks is used in conjunction with biologically inspired techniques to produce novel objects;
- Systems that resort to biologically inspired computation to perform the analysis of image, music, sound, sculpture, or some other types of artistic object.
1 November 2013 11 November 2013
Notification: 06 January 2014
Camera ready: 01 February 2014
Evo*: 23-25 April 2014
Additional information and submission details
Submit your manuscript, at most 12 A4 pages long, in Springer LNCS format (instructions downloadable from http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0) no later than November 11th, 2013.
Submission link: http://myreview.csregistry.org/evomusart14
page limit: 12 pages
The reviewing process will be double-blind; please omit information about the authors in the submitted paper.
To be confirmed
EvoMUSART Conference chairs
University of A Coruna, Spain
University College Dublin, Ireland
EvoMUSART Publication chair
University of Coimbra
Thursday 24 April
1135-1315 Session 1: Aesthetics (Chair: Jon McCormack)
Feature Construction using Genetic Programming for Classification of Images by Aesthetic Value
Andrew Bishop, Vic Ciesielski and Karen Trist
Classification or rating of images according to their aesthetic quality has applications in areas such as image search, compression and photography. It requires the construction of features that are predictive of the aesthetic quality of an image. Constructing features manually for aesthetics prediction is challenging. We propose an approach to improve on manually designed features by constructing them using genetic programming and image processing operations implemented using OpenCV. We show that this approach can produce features that perform well. Classification accuracies of up to 81% on photographs and 92% on computationally generated images have been achieved. Both of these results significantly improve on existing manually designed features.
A complexity approach for identifying aesthetic composite landscapes
Adrian Carballal, Rebeca Perez, Antonino Santos and Luz Castro
The present paper describes a series of features related to complexity which may allow to estimate the complexity of an image as a whole, of all the elements integrating it and of those which are its focus of attention. Using a neural network to create a classifier based on those features an accuracy over 85% in an aesthetic composition binary classification task is achieved. The obtained network seems to be useful for the purpose of assessing the Aesthetic Composition of landscapes. It could be used as part of a media device for facilitating the creation of images or videos with a more professional aesthetic composition.
Authorship and aesthetics experiments. Comparison of results between Human and Computational Systems.
Luz Castro, Rebeca Perez, Antonino Santos and Adrian Carballal
This paper presents the results of two experiments comparing the functioning of a computational system and a group of humans when performing tasks related to art and aesthetics. The first experiment consists of the identification of a painting, while the second one uses the Maitland Graves's aesthetic appreciation test. The proposed system employs a series of metrics based on complexity estimators and low level features. These metrics feed a learning system using neural networks. The computational approach achieves similar results to those achieved by humans, thus suggesting that the system captures some of the artistic style and aesthetics features which are relevant to the experiments performed.
1430-1610 Session 2: Interaction (Chair: James McDermott)
Probabilistic Decision Making for Interactive Evolution with Sensitivity Analysis (EvoMUSART best paper candidate)
Jonathan Eisenmann, Matthew Lewis and Rick Parent
Recent research in the area of evolutionary algorithms and interactive design tools for ideation has investigated how sensitivity analysis can be used to enable region-of-interest selection on design candidates. Even though it provides more precise control over the evolutionary search to the designer, the existing methodology for this enhancement to evolutionary algorithms does not make full use of the information provided by sensitivity analysis and may lead to premature convergence. In this paper, we describe the shortcomings of previous research on this topic and introduce an approach that mitigates the problem of early convergence. A discussion of the trade-offs of different approaches to sensitivity analysis is provided as well as a demonstration of this new technique on a parametric model built for character design ideation.
Feature Selection and Novelty in Computational Aesthetics
(EvoMUSART best paper candidate)
Penousal Machado, Tiago Martins, Hugo Amaro and Pedro H. Abreu
Fitness assignment is one of the biggest challenges in evolutionary art. Interactive evolutionary computation approaches put a significant burden on the user, leading to human fatigue. On the other hand, autonomous evolutionary art systems usually fail to give the users the opportunity to express and convey their artistic goals and preferences. Our approach empowers the users by allowing them to express their intentions through the design of fitness functions. We present a novel responsive interface for designing fitness function in the scope of evolutionary ant paintings. Once the evolutionary runs are concluded, further control is given to the users by allowing them to specify the rendering details of selected pieces. The analysis of the experimental results highlights how fitness function design influences the outcomes of the evolutionary runs, conveying the intentions of the user and enabling the evolution of a wide variety of images.
1630-1810 Session 3: Miscellaneous (Chair: Gary Greenfield)
Evolving an Aircraft Using a Parametric Design System.
Jonathan Byrne, Philip Cardiff, Anthony Brabazon and Michael O'Neill
Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an evolutionary algorithm provides a methodology for optimising designs. This works uses NASA's parametric aircraft design tool (OpenVSP) and an evolutionary algorithm to evolve a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency.
A Novelty Search and Power-Law-Based Genetic Algorithm for Exploring Harmonic Spaces in J.S. Bach Chorales
Bill Manaris, David Johnson and Yiorgos Vassilandonakis
We present a novel, real-time system, called Harmonic Navigator, for exploring the harmonic space in J.S. Bach Chorales. This corpus-based environment explores trajectories through harmonic space. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the 371 J.S. Bach Chorales of the Riemenschneider edition. Our system utilizes a hybrid novelty search approach combined with power-law metrics for evaluating fitness of individuals, as a search termination criterion. We explore how novelty search can aid in the discovery of new harmonic progressions through this space as represented by a Markov model capturing probabilities of transitions between harmonies. Our results demonstrate that the 371 Bach Chorale harmonic space is rich with novel aesthetic possibilities, possibilities that the grand master himself never realized.
Balancing Act: variation and utility in Evolutionary Art
Evolutionary Art typically involves a tradeoff between the size and flexibility of genotype space and its mapping to an expressive phenotype space. Ideally we would like a genotypic representation that is terse but expressive, that is, we want to maximise the useful variations the genotype is capable of expressing in phenotype space. Terseness is necessary to minimise the size of the overall search space, and expressiveness can be loosely interpreted as phenotypes that are useful (of high fitness) and diverse (in feature space). In this paper I describe a system that attempts to maximise this ratio between terseness and expressiveness. The system uses a binary string up to any maximum length as the genotype. The genotype string is interpreted as building instructions for a graph, similar to the cellular programming techniques used to evolve artificial neural networks. The graph is then interpreted as a form-building automaton that can construct animated 3-dimensional forms of arbitrary complexity. In the test case the requirement for expressiveness is that the resultant form must have recognisable biomorphic properties and that every possible genotype must fulfil this condition. After much experimentation, a number of constraints in the mapping technique were devised to satisfy this condition. These include a special set of geometric building operators that take into account morphological properties of the generated form. These methods were used in the evolutionary artwork 'Codeform', developed for the Ars Electronica museum. The work generated evolved virtual creatures based on genomes acquired from the QR codes on museum visitor's entry tickets.