Interactive evolutionary 3D fractal modeling.

Pang, Wenjun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 83-88).Abstracts in English and Chinese.ACKNOWLEDGEMENTS --- p.iiABSTRACT --- p.iv摘要 --- p.vCONTENTS --- p.viList of Tables --- p.viiiList of Figures --- p.ixChapter 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Recent research work --- p.4Chapter 1.2 --- Objectives --- p.8Chapter 1.3 --- Thesis Organization --- p.10Chapter 2. --- FRACTAL MODELING --- p.12Chapter 2.1 --- Fractal and Fractal Art --- p.12Chapter 2.2 --- Fractal Geometry --- p.15Chapter 2.3 --- Construction of Fractals --- p.21Chapter 2.4 --- Fractal Measurement and Aesthetics --- p.27Chapter 3. --- OVERVIEW OF EVOLUTIONARY DESIGN --- p.30Chapter 3.1 --- Initialization --- p.33Chapter 3.2 --- Selection --- p.33Chapter 3.3 --- Reproduction --- p.34Chapter 3.4 --- Termination --- p.36Chapter 4. --- EVOLUTIONARY 3D FRACTAL MODELING --- p.38Chapter 4.1 --- Fractal Construction --- p.38Chapter 4.1.1 --- Self-similar Condition of Fractal --- p.38Chapter 4.1.2 --- Fractal Transformation (FT) IFS Formulation --- p.39Chapter 4.1.3 --- IFS Genotype and Phenotype Expression --- p.41Chapter 4.2 --- Evolutionary Algorithm --- p.43Chapter 4.2.1 --- Single-point Crossover --- p.45Chapter 4.2.2 --- Arithmetic Gaussian mutation --- p.45Chapter 4.2.3 --- Inferior Elimination --- p.46Chapter 4.3 --- Interactive Fine-tuning using FT IFS --- p.46Chapter 4.4 --- Gaussian Fitness Function --- p.48Chapter 5. --- GAUSSIAN AESTHETIC FITNESS FUNCTION --- p.49Chapter 5.1 --- Fitness Considerations --- p.50Chapter 5.2 --- Fitness Function Formulation --- p.53Chapter 5.3 --- Results and Discussion on Fitness Function --- p.55Chapter 6. --- EXPERIMENT RESULTS and DISCUSSION --- p.59Chapter 6.1 --- Experiment of Evolutionary Generation --- p.59Chapter 6.2 --- Comparison on Different Methods --- p.60Chapter 7. --- 3D FRACTALS RENDERING and APPLICATION --- p.62Chapter 7.1 --- Transforming Property and User Modification --- p.62Chapter 7.2 --- Visualization and Rendering of 3D Fractals --- p.66Chapter 7.3 --- Applications in Design --- p.74Chapter 8. --- CONCLUSIONS and FUTURE WORK --- p.81Chapter 8.1 --- Conclusions --- p.81Chapter 8.2 --- Future Work --- p.81BIBLIOGRAPHY --- p.83Appendix --- p.89Marching Cubes Method --- p.8

Automatic Generation of Aesthetic Patterns with the Use of Dynamical Systems

The aim of this paper is to present some modifications of the orbits generation algorithm of dynamical systems. The well-known Picard iteration is replaced by the more general one - Krasnosielskij iteration. Instead of one dynamical system, a set of them may be used. The orbits produced during the iteration process can be modified with the help of a probabilistic factor. By the use of aesthetic orbits generation of dynamical systems one can obtain unrepeatable collections of nicely looking patterns. Their geometry can be enriched by the use of the three colouring methods. The results of the paper can inspire graphic designers who may be interested in subtle aesthetic patterns created automatically

Procedural Generation of Artistic Patterns Using a Modified Orbit Trap Method

In the literature, we can find various methods for generating artistic patterns. One of the methods is the orbit trap method. In this paper, we propose various modifications of a variant of the orbit trap method that generates patterns with wallpaper symmetry. The first modification relies on replacing the Picard iteration (used in the original method) with the S-iteration known from the fixed point theory. Moreover, we extend the parameters in the S-iteration from scalar to vector ones. In the second modification, we replace the Euclidean metric used in the orbit traps with other metrics. Finally, we propose three new orbit traps. The presented examples show that using the proposed method, we are able to obtain a great variety of interesting patterns. Moreover, we show that a proper selection of the orbit traps and the mapping used by the method can lead to patterns that possess a local fractal structure.Natural Science Foundation of China grant number 6206204

Evaluating complex engineered systems using complex network representations

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Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design

Algorytmy ewolucyjne w projektowaniu fraktalnych wzorów użytkowych

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