Penousal Machado, Juan Romero



Part I   Evolutionary Art


1 Evolutionary Visual Art and Design

Matthew Lewis

Summary. This chapter presents an introduction to the different artistic design domains that make use of interactive evolutionary design approaches, the techniques they use, and many of the challenges arising. After a brief introduction to concepts and terminology common to most artificial genetic design, there is a survey of artistic evolutionary systems and related research for evolving images and forms. While the focus is primarily on purely aesthetic fitness landscapes, the survey also ventures into areas such as product design and architecture. The overview shifts from technique to application as organizational strategies, as appropriate. After briefly surveying additional information sources, the chapter concludes with a discussion of major topics of relevance to evolutionary system designers, providing context for the following chapters. It is hoped that this snapshot of the state of the field will increase exposure to projects and issues, discussion amongst participants, and ultimately the accessibility of these techniques and approaches.


2 Evolutionary Search for the Artistic Rendering of Photographs

J. P. Collomosse

Summary. This chapter explores algorithms for the artistic stylization (transformation) of photographs into digital artwork, complementing techniques discussed so far in this book that focus on image generation. Most artistic stylization algorithms operate by placing atomic rendering primitives “strokes” on a virtual canvas, guided by automated artistic heuristics. In many cases the stroke placement process can be phrased as an optimization problem, demanding guided exploration of a high dimensional and turbulent search space to produce aesthetically pleasing renderings. Evolutionary search algorithms can offer attractive solutions to such problems.
This chapter begins with a brief review of artistic stylization algorithms, in particular algorithms for producing painterly renderings from two-dimensional sources. It then discusses how genetic algorithms may be harnessed both to increase control over level of detail when painting (so improving aesthetics) and to enhance usability of parameterized painterly rendering algorithms.


3 Evolution and Collective Intelligence of the Electric Sheep

Scott Draves

Summary. Electric Sheep is a collective intelligence composed of 40,000 computers and people mediated by a genetic algorithm. It is made with an open source screensaver that harnesses idle computers into a render farm with the purpose of animating and evolving artificial life-forms known as sheep. The votes of the users form the basis for a fitness function for exploring a space of abstract animations. Users also may design sheep by hand for inclusion in the gene pool.
The name Electric Sheep is an homage to Philip K. Dick's novel Do Androids Dream of Electric Sheep; the basis for the film Blade Runner. The metaphor compares the screen-saver to the computer's dream.
After the introduction, we dig into the system starting with Sect. 3.2 on its architecture and implementation. Sect. 3.3 covers the genetic code, including its basis in the equations of classic Iterated Function Systems. The equation is then generalized into the Fractal Flame algorithm, which translates the genetic code into an image. The next two sections treat color and motion.
Section 3.4 shows how the genetic algorithm decides which sheep die, which ones reproduce, and how. Section 3.5 defines the primary dataset and its limitations, and reports some of its statistics. Section 3.5.1 uses the dataset to determine that the genetic algorithm functions more as an amplifier of its human collaborators' creativity rather than as a traditional genetic algorithm that optimizes a fitness function.
The goal of Electric Sheep is to create a self-supporting, network-resident lifeform. Section 3.6 speculates on how to make the flock support more of a selfsustaining reaction rather than functioning as an amplifier. Finally, Sect. 3.6.1, explains how Dreams in High Fidelity addresses the support issue.



Part II   Evolutionary Music


4 Evolutionary Computation Applied to Sound Synthesis

Summary. Sound synthesis is a natural domain in which to apply evolutionary computation (EC). The EC concepts of the genome, the phenotype, and the fitness function map naturally to the synthesis concepts of control parameters, output sound, and comparison with a desired sound. More importantly, sound synthesis can be a very unintuitive technique, since changes in input parameters can give rise, via non-linearities and interactions among parameters, to unexpected changes in output sounds. The novice synthesizer user and the simple hill-climbing search algorithm will both fail to produce a desired sound in this context, whereas an EC technique is well-suited to the task.
In this chapter we introduce and provide motivation for the application of EC to sound synthesis, surveying previous work in this area. We focus on the problem of automatically matching a target sound using a given synthesizer. The ability to mimic a given sound can be used in several ways to augment interactive sound synthesis applications. We report on several sets of experiments run to determine the best EC algorithms, parameters, and fitness functions for this problem.


5 Swarm Granulation

Tim Blackwell

Summary. Swarm granulation, as a union of swarming behaviour and sonic granulation, holds much potential for the generation of novel sounds. Theoretical and practical aspects of this technique are outlined here, and an explanation of how musical interactions with swarms can be enabled using an analogue of the biological process of stigmergy. Two manifestations of swarm granulation are explained in some detail. Social criticality, an inter-particle communication that is driven by a critical system such as a sandpile, and its use in determining the rendering of sound grains, is introduced.


6 Evolutionary Computing for Expressive Music Performance

Summary. We describe an evolutionary approach to one of the most challenging problems in computer music: modeling the knowledge applied by a musician when performing a score of a piece in order to produce an expressive performance of the piece. We extract a set of acoustic features from jazz recordings, thereby providing a symbolic representation of the musician’s expressive performance. By applying an evolutionary algorithm to the symbolic representation, we obtain an interpretable expressive performance computational model. We use the model to generate automatically performances with the timing and energy expressiveness of a human saxophonist.



Part III   Real World Applications


7 Evolutionary and Swarm Design in Science, Art, and Music

Summary. Evolutionary design can take many forms. In this chapter, we describe how different evolutionary techniques — such as genetic programming and evolution strategies — can be applied to a wide variety of nature-inspired designs. We will show how techniques of interactive evolutionary breeding can facilitate the creative processes of design. As practical examples we demonstrate how to use implicit surface modeling to create virtual sculptures, and furniture designs through evolutionary breeding.
Rather than creating variations of blueprints through an evolutionary process, we then focus on the evolution of ‘design programs’. That is, instead of a static description (blueprint) of an object, we evolve recipes or algorithms to build objects. This leads to a much wider repertoire of variability on the designer’s side and can be implemented in a straightforward manner using genetic programming. Starting with a simple breeding approach of fractals, we give examples of how to — either automatically or interactively — evolve growth programs for plants with particular characteristics, which we illustrate using a garden of artificial flowers. We use evolvable Lindenmayer systems (L-systems) to capture growth processes.
The evolution of choreographic swarm interactions leads to new ways of ‘swarm programming’, where changes in control parameters result in emergent agent behaviours. Swarm grammars, as we will show, combine swarming agents with developmental programs as an extension of L-systems. We demonstrate how to use this technique to generate virtual paintings on 2D and 3D canvases. These SwarmArt implementations have also been exhibited in various museums as interactive computer installations, which we will use to describe how to integrate music and sound generation into evolutionary swarm systems.


8 Genr8: Architects’ Experience with an Emergent Design Tool

Summary. We present the computational design tool Genr8 and six different architectural projects making extensive use of Genr8. Genr8 is based on ideas from Evolutionary Computation (EC) and Artificial Life and it produces surfaces using an organic growth algorithm inspired by how plants grow. These algorithms have been implemented as an architect’s design tool and the chapter provides an illustration of the possibilities that the tool provides.


9 Evolving Human Faces

Summary. Witnesses and victims of serious crime are normally requested to construct a picture of the criminal’s face. These pictures are known as facial composites and are typically produced by a witness recalling details of the face and then selecting individual facial features: hair, eyes, nose, mouth, etc. While composites remain an important tool for the apprehension of criminals, research has suggested that, even under favourable conditions, they are rarely recognised. In the current chapter, we present a new method called EvoFIT whereby users select complete faces and a composite is “evolved” using a Genetic Algorithm. While considerable development was required to tune the new approach, research indicates that EvoFIT now produces more identifiable composites than those produced from the traditional “feature” systems. Novel applications of the technology are also discussed.


10 Evolutionary Reproduction of Dutch Masters: The Mondriaan and Escher Evolvers

A.E. Eiben

Summary. Creative evolutionary systems are often concerned with producing images of high artistic quality. A key challenge to such a system is to be humancompetitive by producing the same quality. Then, mimicking existing human artists could be seen as a canonical benchmark, not unlike the Turing test for intelligence. This chapter discusses two applications aimed at evolving images in the styles of two well-known Dutch painters: Mondriaan and Escher. For both cases we have an evaluation criterion based on “style-fidelity” as perceived and judged by the users. In other words, here we have a target style, which makes the (subjective) selection less free than in applications solely aiming at nice images. Technically, the Mondriaan evolver is less difficult, given that his most popular style “simply” uses horizontal and vertical lines, and primary colours to fill the resulting rectangles. The Escher evolver project is more challenging. First, because Escher’s style is less simple to capture. Second, the system is tested in vivo, in a real museum, posing requirements on the interface. We describe how to meet the style challenge based on the mathematical system behind Escher’s tiling. Designing a suitable representation and the corresponding variation operators based on this system specifies an appropriate search space guaranteeing the Escher style to some extent and leaving enough freedom for the selection. As for the second objective, we describe two versions that differ in the way the images are presented to, respectively evaluated by the visitors. The experiences gained during a six-month exhibition period in the City Museum in The Hague, The Netherlands, are discussed from the visitors’ perspective as well as from the algorithmic point of view and are illustrated with some “evolved Escher’s”.



Part IV   Artistic Perspectives


11 Artificial Art made by Artificial Ants

Summary. We present how we have considered the artificial ant paradigm as a tool for the generation of music and painting. From an aesthetic perspective, we are interested in demonstrating that swarm intelligence and self-organization can lead to spatio-temporal structures that can reach an artistic dimension. In our case, the use of artificial pheromones can lead to the creation of melodies thanks to a cooperative behavior of the ant-agents but also to the emergence of abstract paintings thanks to competitive behaviors within the artificial colonies. The user’s point of view is also taken into account through interactive genetic algorithms.


12 Embedding of Pixel-Based Evolutionary Algorithms in My Global Art Process

Günter Bachelier

Summary. My art comprises three levels: basic, methodical, and superordinate. The basic level refers to the production of individual works of art. At the methodical level, I am concerned with the development of artistic processes, in this case, inspired by evolutionary concepts. Finally, at the superordinate level, social sculpture, “Health Art”, is considered.
Between 1995 and 2003 I developed and used several pixel-based evolutionary art processes. These artistic approaches resort to biologically inspired concepts, such as population, variation, and selection. The individuals were pixel images. In the reproduction phase two (or more) individuals were selected as parents and the images were recombined by the exchange of image parts (Regions-of-Interest). Variation (mutation) was performed by means of image processing operations such as the rotation of image parts with random but constrained parameters. Parents and offspring were evaluated by the aesthetic preferences of the artist and the best individuals built the next generation.
A unique and more complex evolution art process was developed in 2004, with no direct correspondence to known evolutionary algorithms like GA, ES, or GP. It uses some additional evolutionary concepts, such as a global pool of images and multi-sexual recombination, together with ontogenetic concepts, such as spores or fruits, and other concepts such as image templates. In a narrow sense, there is no change of generations of image individuals. Selected individuals are directly inserted into the global image pool, being also involved in a second reproduction process where they are transformed by mathematical symmetry operations. The resulting images are also inserted into the global image pool, while all the images in this pool build the basis for recombination in the next generation.
Future plans, about file formats, other image reproduction operators, image databases, and aesthetic preference modelling, are also discussed.


13 Evolving Structure in Liquid Music

J. J. Ventrella

Summary. A software application called “Musical Gene Pool” is described. It was designed to evolve non-linear music from an initial random soup of sounds, which play continuously. Most evolutionary music systems to date require the user to select for musical aspects in a piecemeal fashion, whereas this system is experienced as continuous music throughout the entire process, as follows: a human listener gives fitness rewards after sounds (organisms) emerge from the gene pool, take turns playing, and return back to the pool. Organisms start out unicellular (one sound), but as the listener selectively rewards random sequences deemed more musical than others, some organisms join up to form larger, multicellular organisms – which become phrases or extended musical gestures. Genetic operators of splitting, death, replication, and mutation occur in the gene pool among rewarded organisms. This results in gradual evolution of structure as the music continues to play. This emerges in response to the listener’s own internal emerging musical language, based on accumulated musical memory. This structure is liquid – continually able to flow and rearrange to allow serendipity. While there is a limit to organism length (duration of phrases), it is proposed that the interactive scheme could be adjusted to evolve increasingly larger organisms, and hence, longer musical passages. These would essentially be mobile chunks of linear music with self-similarity in their structures – revealing the histories of their evolution.


14 A Survey of Virtual Ecosystems in Generative Electronic Art

Alan Dorin

Summary. This paper explores the application of ecosystem simulation to the production of works of generative electronic art. The aim is to demonstrate that virtual ecosystems are capable of producing outcomes that are rich, complex and interesting aesthetically. A number of artworks that employ virtual ecosystems are surveyed. The author argues that the most interesting works of generative art exhibit four basic properties: coherence and unity; multi-scaled temporal complexity; autonomous production of novelty; responsiveness to perturbation. The virtual ecosystem is assessed for its suitability as a medium for constructing generative art in light of these desirable properties. It is concluded that the ecosystem’s strengths lie in its exhibition of multi-scaled complexity and its autonomous production of novelty. Whilst an artist may manipulate a simulation to retain visual and sonic coherence, the software also possesses an implicit coherence inherent in its ability to self-organize. Under some circumstances it appears that the weakness of the virtual ecosystem as an artistic medium lies in its unpredictable response to perturbation. Consequently, the paper also explores virtual ecosystems’ susceptibility to external control and describes methods that have been employed to adjust the responsiveness of art works that employ them.


15 Complexism and the Role of Evolutionary Art

Philip Galanter

Summary. Artists have always learned from nature. A new generation of artists is adapting the very processes of life to create exciting new works. But art is more than the creation of objects. It is also a progression of ideas with a history and a correspondence to the larger culture.
The goal of this chapter is to take a step back from the details of the technology and the consideration of specific works, and to view evolutionary art in the broader context of all art. This kind of multidisciplinary discussion requires one to be multilingual, and this chapter will use the language of scientists, humanists, artists, and philosophers. While doing so we will quickly visit complexity science, postmodernism in the arts, and the conflict between the cultures of the humanities and the sciences.
With this as a backdrop, I will introduce a new approach I call complexism. Complexism is the application of a scientific understanding of complex systems to the subject matter of the arts and humanities. We will see that the significance of evolutionary art is that it takes complexism as both its method and content. Evolutionary art is a new kind of dynamic iconography: the iconography of complexism. And complexism offers nothing less than the reconciliation of the sciences and the humanities through a higher synthesis of the modern and the postmodern.
To a certain extent this chapter participates in the modernist tradition of the art manifesto. The art manifesto is a form of speculative writing where the artist-author posits a new revolutionary creative direction for a group of artists who share a set of common interests, as well as a new worldview that offers a radical break with the past. Writers of such manifestos have included Marinetti, Kandinsky, Schwitters, Moholy-Nagy, Gropius, Breton, and others.
Like other manifestos, this chapter includes forward-looking assertions about work not yet started let alone completed. I have tried to identify the more speculative parts of this chapter as being part of this complexist manifesto.



Part V   Future Perspectives


16 The Evolution of Artistic Filters

Summary. Artistic image filters are evolved using genetic programming. The system uses automatic image analysis during fitness evaluation. Multi-objective optimization permits multiple feature tests to be applied independently. One unique fitness test is Ralph’s bell curve model of aesthetics. This model is based on an empirical evaluation of hundreds of fine art works, in which paintings have been found to exhibit a bell curve distribution of color gradient. We found that this test is very useful for automatically evolving non-photorealistic filters that tend to produce images with painterly, balanced and harmonious characteristics. The genetic programming language uses a variety of image processing functions of varying complexity, including a higher-level paint stroke operator. The filter language is designed so that components can be combined together in complex and unexpected ways. Experiments resulted in a surprising variety of interesting “artistic filters”, which tend to function more like higher-level artistic processes than low-level image filters. Furthermore, a correlation was found between an image having a good aesthetic score, and its application of the paint operator.


17 Co-evolutionary Methods in Evolutionary Art

Gary R. Greenfield

Summary. Following the ground breaking work of Sims and Latham there was a flurry of activity in interactive artificial evolution of images. However, the move towards non-interactive evolution of images that arises by invoking fitness functions to serve in place of users in order to guide simulated evolution proceeded haltingly and unevenly. If evolutionary computational models for image evolution are indeed inspired by nature, then it is natural to consider image evolution in the broader coevolutionary context. This chapter briefly surveys the role co-evolutionary methods have played in evolutionary computation and then examines some of the instances where it has been applied to evolutionary art. The paucity of examples leads to a discussion of the challenges faced, and the difficulties encountered, when trying to use co-evolutionary methods both in evolutionary art and artificial creativity.


18 Experiments in Computational Aesthetics

Summary. A novel approach to the production of evolutionary art is presented. This approach is based on the promotion of an arms race between an adaptive classifier and an evolutionary computation system. An artificial neural network is trained to discriminate among images previously created by the evolutionary engine and famous paintings. Once training is over, evolutionary computation is used to generate images that the neural network classifies as paintings. The images created throughout the evolutionary run are added to the training set and the process is repeated. This iterative process leads to the refinement of the classifier and forces the evolutionary algorithm to explore new paths. The experimental results attained across iterations are presented and analyzed. Validation tests were conducted in order to assess the changes induced by the refinement of the classifier and to identify the types of images that are difficult to classify. Taken as a whole, the experimental results show the soundness and potential of the proposed approach.


19 Facing the Future: Evolutionary Possibilities for Human-Machine Creativity

Jon McCormack

Summary. This chapter examines the possibilities and challenges that lie ahead for evolutionary music and art. Evolutionary computing methods have enabled new modes of creative expression in the art made by humans. One day, it may be possible for computers to make art autonomously. The idea of machines making art leads to the question: what do we mean by ‘making art’ and how do we recognise and acknowledge artistic creativity in general? Two broad categories of human-machine creativity are defined: firstly, machines that make art like, and for, humans; and secondly, machines that make ‘art’ that is recognised as creative and novel by other machines or agents. Both these categories are examined from an evolutionary computing perspective. Finding ‘good’ art involves searching a phase-space of possibilities beyond astronomical proportions, which makes evolutionary algorithms potentially suitable candidates. However, the problem of developing artistically creative programs is not simply a search problem. The multiple roles of interaction, environment, physics and physicality are examined in the context of generating aesthetic output. A number of ‘open problems’ are proposed as grand challenges of investigation for evolutionary music and art. For each problem, the impetus and background are discussed. The paper also looks at theoretical issues that might limit prospects for art made by machines, in particular the role of embodiment, physicality and morphological computation in agent-based and evolutionary models. Finally, the paper looks at artistic challenges for evolutionary music and art systems.




(c) Juan Romero & Penousal Machado. 2007. The Art of Artificial Evolution