We live in a world characterized by evolution -- that is, by ongoing processes of development, formation, and growth in both natural and human-created systems. Biology tells us that complex, natural systems are not created all at once but must instead evolve over time. We are becoming increasingly aware that evolutionary processes are ubiquitous and critical for technological innovations as well. This is particularly true for complex software systems because these systems do not necessarily exist in a technological context alone but instead are embedded within dynamic human organizations. The Center for LifeLong Learning and Design (L 3 D) at the University of Colorado has been involved in research on software design and other design domains for more than a decade. We understand software design as an evolutionary process in which system requirements and functionality are determined through an iterative process of collaboration among multiple stakeholders, rather than being completel...

This paper explores the application of interactive genetic algorithms to the creation of line drawings. We have built a system that starts with a collection of drawings that are either randomly generated or input by the user. The user selects one such drawing to mutate or two to mate, and a new generation of drawings is produced by randomly modifying or combining the selected drawing(s). This process of selection and procreation is repeated many times to evolve a drawing. A wide variety of complex sketches with highlighting and shading can be evolved from very simple drawings. This technique has enormous potential for augmenting and enhancing the power of traditional computer-aided drawing tools, and for expanding the repertoire of the computer-assisted artist.

How do genetic systems gain information by evolutionary processes? Answering this question precisely requires a robust, quantitative measure of information. Fortunately, 50 years ago Claude Shannon defined information as a decrease in the uncertainty of a receiver. For molecular systems, uncertainty is closely related to entropy and hence has clear connections to the Second Law of Thermodynamics. These aspects of information theory have allowed the development of a straightforward and practical method of measuring information in genetic control systems. Here this method is used to observe information gain in the binding sites for an artificial `protein' in a computer simulation of evolution. The simulation begins with zero information and, as in naturally occurring genetic systems, the information measured in the fully evolved binding sites is close to that needed to locate the sites in the genome. The transition is rapid, demonstrating that information gain can occur by punctuated equilibrium.

Cellular automata are dynamical systems in which space and time are discrete, where each cell obeys the same rule and has a finite number of states. In this paper we study non-uniform cellular automata, i.e. with non-uniform local interaction rules. Two different models are described. In the first a cell's rule may be regarded as a genotype whose phenotypic effect is achieved by rule application. Our focus is on evolution in rule space starting from a random gene pool, i.e. rule population. The second model focuses on the study of complex structures formed by a small number of rules, where the term `complex' denotes a structure consisting of simple grid cells, acting as a single "organism ". 1 Introduction Cellular automata (CA) are dynamical systems in which space and time are discrete. The states of cells in a regular grid are updated synchronously according to a local interaction rule. Each cell obeys the same rule and has a finite (usually small) number of states (Toffoli and Marg...

The power of the unaided, individual mind is highly overrated: The Renaissance scholar no longer exists. Although creative individuals are often thought of as working in isolation, the role of interaction and collaboration with other individuals is critical to creativity. Creative activity grows out of the relationship between individuals and their work, and from the interactions between an individual and other human beings. Because complex problems require more knowledge than any single person possesses, it is necessary that all involved stakeholders participate, communicate, collaborate, and learn from each other. Distances (across spatial, temporal, and technological dimensions) and diversity (bringing stakeholders together from different cultures) are important sources for social creativity. This paper describes conceptual frameworks and sociotechnical environments (derived from the systems that we have developed over the last decade) in which social creativity can come alive. Keywords design, social creativity, spatial distance, temporal distance, technological distance, diversity, communities of practice, communities of interest, division of labor

The use of Evolutionary Computation approaches to generate images has reached a great popularity. This led to the emergence of a new art form -- Evolutionary Art -- and to the proliferation of Evolutionary Art Tools. In this paper, we present an Evolutionary Art Tool, NEvAr, the experimental results achieved, and the work methodology used to generate images. In NEvAr, useful individuals are stored in a database in order to allow their reuse. This database is playing an increasingly important role in the creation of new images, which led us to the development of automatic seeding procedures, also described. The automation of fitness assignment is one of our present research interests. We will, therefore, describe some preliminary results achieved with our current approach to automatic evaluation.

The use of Evolutionary Computation approaches to create images has reached a great popularity, leading to the appearance of a new art form -- Evolutionary Art -- and to the proliferation of Evolutionary Art Tools. In this paper we present and make an assessment of one of these tools: NEvAr. We also systematise and describe the work methodology currently used to generate images. When working with NEvAr we focus on the reuse of useful individual, which we store in an image database. The size of this database, and the importance of its role, led us to the development of automatic seeding procedures, which we also describe.

I try to clarify several confusions in the popular literature concerning chaos, determinism, the arrow of time, entropy and the role of probability in physics. Classical ideas going back to Laplace and Boltzmann are explained and defended while some recent views on irreversibility, due to Prigogine, are criticized.

This paper proposes the use of a genetic algorithm to develop neural networks to play the Capture Game, a subgame of Go. The motivation for this is twofold: to evaluate and possibly improve upon current genetic algorithm variants in order to produce a good player and (more importantly) to use this process to examine the properties and processes that are present in evolutionary systems in an attempt to shed some light on the phenomena that are required for an evolutionary process to produce robust, perpetually improving individuals and avoid local minima without any outside interaction. A brief survey of related work in the area is given, which highlights some of the interesting research questions that remain. This is followed by an outline of a distributed system that has been developed for use in the experimental evaluation of some of the proposed ideas and some of the initial results generated by the system. 1

This paper is dedicated to the development of constructed artists, i.e., computer programs capable of creating artworks with little or no human intervention. We make an analysis and critic of some of the most prominent work on this field. We give a description of the main characteristics that a system should have, in order to be considered a constructed artist. These characteristics include the capacity of making aesthetic judgments, which takes us to the origins of art and aesthetics, for which we present a brief theory. Finally we propose a model for the development of a constructed artist. This model has the capability of performing aesthetic evaluation, through the use of neural networks. The images are generated using a genetic algorithm, and represented using Fractal Image Encoding. This type of methodology allows the representation and, consequently, the generation of any type of image.