Autonomous Evolutionary Art

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Eelco Heijer: Autonomous Evolutionary Art. PhD Thesis, VRIJE UNIVERSITEIT, 2013, ISBN 9789461919519.

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Abstract

We begin this thesis with this introduction, next we describe our evo- lutionary art system, the Art Habitat in Chapter 2. Although we describe the use of genotype representation extensively in part II, we describe the use of evolving symbolic expression already in Chapter 2, because several sections in Part I depend on the description of the function set of the Art Habitat. Next, we present a brief chapter on the relation between evolutionary art and aesthetics in Chapter 3. This thesis is divided into three main parts; the first part deals with fitness, and contains chapters on our investigations into the use of several aesthetic measures as fitness functions in autonomous EvoArt systems. The second part is about genotype representation; the most popular forms of genotype representation is the standard expression tree that is common in the field of genetic programming (GP). Chap- ter 4 contains an overview of seven aesthetic measures; we describe their technical implementation, and we perform experiments with these aesthetic measures in our EvoArt systems. One of the major outcomes of this research and of earlier papers [dHE10a, dHE10b] was that the choice of the aesthetic measure has a profound influ- ence on the ‘style’ of the evolved images. Our next major ques- tion was whether it was possible to combine multiple styles (or features) into images using multiple aesthetic measures in a multi- objective optimisation setup. We describe our findings in Chapter 6. One of the outcomes of these experiments (originally published in [dHE11a]) was that constructing the combination of aesthetic mea- sures is far from trivial. Several combinations of aesthetic measures work counter-productive because the aesthetic measures (in the com- bination) search in different directions within the same image feature subspace (e.g. colour or contrast). With this finding in mind, we thought of the idea to devise an aesthetic measure that acts on a dif- ferent part of the search space than most aesthetic measures, and we devised an aesthetic measure that acts on symmetry and one that acts on the compositional balance of the image [dHar] These two aesthetic measures are described in Chapter 5. We extended the multi-objective investigations of the original paper [dHE11a] with the aesthetic mea- sures from Chapter 5 and the original and new experiments with multi-objective optimisation are described in Chapter 6. We conclude Part I on fitness with several ideas for future work in Chapter 7. From our initial experiments we engaged a number of recurring is- sues. First of all, despite the variety of functions in our function sets, the different colour schemes and different aesthetic measures as fitness functions, we felt that the evolved images were somehow stuck in a sort of ‘computer art’ local optimum. Jon McCormack ob- served similar findings [McC05, McC07], as did a number of others [Par08, Gal10]. We decided to investigate the possibilities of find- ing new, more powerful genotype representations, and our findings are described in Part II on representation. Chapter 9 describes our research into using Scalable Vector Graphics as a genotype represen- tation in our EvoArt system. We use SVG to evolve abstract and representational (or figurative) images. Chapter 10 describes another genotype representation that uses a very recent computer graphics technique called ‘Glitch’. Another finding from our initial experiments is that experiments in autonomous evolutionary art often result in convergence of the en- tire population to a single individual. Most individuals are either copies of that single individual or slight variations. We soon realised that population diversity would be an important issue in our EvoArt system. Part III of this thesis describes our investigations into main- taining population diversity in EvoArt systems. Chapter 12 describes the use of custom genetic operators (initialisation, crossover and mu- tation) that perform a local search in order to increase diversity. In Chapter 13 we describe the use of Cellular Evolutionary Algorithms and Island Models in order to maintain population diversity.

Extended Abstract

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