Evolutionary computing in visual art and music

2009

Abstract A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function.

Computers & Graphics, 2007

The boundaries of art are subjective, but the impetus for art is often associated with creativity, regarded with wonder and admiration along human history. Most interesting activities and their products are a result of creativity. The main goal of our approach is to explore new creative ways of editing and producing videos, using evolutionary algorithms. A creative evolutionary system makes use of evolutionary computation operators and properties and is designed to aid our own creative processes, and to generate results to problems that traditionally required creative people to solve. Our system is able to generate new videos or to help a user in doing so. New video sequences are combined and selected, based on their characteristics represented as video annotations, either by defining criteria or by interactively performing selections in the evolving population of video clips, in forms that can reflect editing styles. With evolving video, the clips can be explored through emergent narratives and aesthetics in ways that may reveal or inspire creativity in digital art. r

Lecture Notes in Computer Science, 2005

Applying evolutionary methods to the generation of music and art is a relatively new field of enquiry. While there have been some important developments, it might be argued that to date, successful results in this domain have been limited. Much of the present research can be characterized as finding adhoc methods that can produce subjectively interesting results. In this paper, it is argued that a stronger overall research plan is needed if the field is to develop in the longer term and attract more researchers. Five 'open problems' are defined and explained as broad principle areas of investigation for evolutionary music and art. Each problem is explained and the impetus and background for it is described in the context of creative evolutionary systems.

2019

Evolutionary Computation techniques have been applied to several fields in the Arts in recent years. This talk overviews how they have been used to create different types of artifacts, and how past developments relate to current approaches, trends and challenges. The focus is to address a main challenge in the field, fitness assignment, analyzing this from the perspective of the interplay between the evolutionary system and the user, and discussing how Machine Learning, Evolutionary Computation and HCI techniques can be combined to create Computer Aided Creativity systems that allow the users to express their artistic or aesthetic intentions.

2000

The interactive evolutionary computation (IEC), i.e., an evolutionary computation whose fitness function is provided by users, has been applied to aesthetic areas, such as art, design and music. We cannot always define fitness functions explicitly in these areas. With IEC, however, the user's implicit preference can be embedded into the optimization system. This paper describes a new approach to the music composition, more precisely the composition of rhythms, by means of IEC. The main feature of our method is to combine genetic algorithms (GA) and genetic programming (GP). In our system, GA individuals represent short pieces of rhythmic patterns, while GP individuals express how these patterns are arranged in terms of their functions.

User fatigue is probably the most pressing problem in current Interactive Evolutionary Computation systems. To address it we propose the use of automatic seeding procedure, phenotype filters, and partial automation fitness assignment. We test this approaches in the visual arts domain. To further enhance interactive evolution applications in aesthetic domains, we propose the use of artificial art critics -systems that perform stylistic and aesthetic valuations of art -presenting experimental results.

2010

In this paper we investigate and compare three aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on the evolved images. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved by measure i according to measure j. This way we investigate the flexiblity of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of images). Last, we perform an image analysis using a fixed set of image statistics functions. The results show that aesthetic measures have a rather clear 'style' and that these styles can be very different. Furthermore we find that some aesthetic measures show little flexibility and appreciate only a limited set of images. The images in this paper might only be in color in the electronic version.

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, 2014

We have modeled the actual process of creating paintings using Evolutionary Computing. In general, an outline and a theme are decided at the beginning of creating a painting, and events are located by the theme, however we evolve initial images to the preselected theme image with this method. We conducted a questionnaire to make the images created by this method to evaluate, and researched which generation people were interested in. We found that the images that have high aesthetic evaluation and originality are created in early and middle generations.

This PhD thesis entitled 'Autonomous Evolutionary Art' investigates various aspects of autonomous (or 'unsupervised', no-human-in-the-loop) evolutionary art. Main topics are computational aesthetics, genetic representations for evolutionary art, and population diversity.