Evolutionary Art Using Summed Multi-objective Ranks: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „ == Reference == Bergen, S., Ross, B.: Evolutionary Art Using Summed Multi-objective Ranks. In: Genetic Programming - Theory and Practice VIII, pp. 227–244…“) |
(kein Unterschied)
|
Aktuelle Version vom 17. November 2014, 13:22 Uhr
Inhaltsverzeichnis
Reference
Bergen, S., Ross, B.: Evolutionary Art Using Summed Multi-objective Ranks. In: Genetic Programming - Theory and Practice VIII, pp. 227–244. Springer (May 2010)
DOI
http://link.springer.com/chapter/10.1007%2F978-1-4419-7747-2_14
Abstract
This paper shows how a sum of ranks approach to multi-objective evaluation is effective for some low-order search problems, as it discourages the generation of outlier solutions. Outliers, which often arise with the traditional Pareto ranking strategy, tend to exhibit good scores on a minority of feature tests, while having mediocre or poor scores on the rest. They arise from the definition of Pareto dominance, in which an individual can be superlative in as little as a single objective in order to be considered undominated. The application considered in this research is evolutionary art, inwhich images are synthesized that adhere to an aesthetic model based on color gradient distribution. The genetic programming system uses 4 different fitness measurements, that perform aesthetic and color palette analyses. Outliers are usually undesirable in this application, because the color gradient distribution measurements requires 3 features to be satisfactory simultaneously. Sum of ranks scoring typically results in images that score better on the majority of features, and are therefore arguably more visually pleasing. Although the ranked sum strategy was originally inspired by highly dimensional problems having perhaps 20 objectives or more, this research shows that it is likewise practical for low-dimensional problems.
Extended Abstract
Bibtex
Used References
Baluja, S., Pomerleau, D., and Jochem, T. (1994). TowardsAutomated Artificial Evolution for Computer-generated Images. Connection Science, 6(2/3):325–354. http://dx.doi.org/10.1080/09540099408915729
Bentley, P. and Corne, D.W. (2002). Creative Evolutionary Systems. Morgan Kaufmann.
Bentley, P.J. and Wakefield, J.P. (1997). Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms. In Soft Computing in Engineering Design and Manufacturing. Springer Verlag.
Coello, C.A. Coello, Lamont, G.B., and Veldhuizen, D.A. Van (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, 2 edition.
Corne, D. and Knowles, J. (2007). Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In Proceedings GECCO 2007, pages 773–780. ACM Press.
Dawkins, R. (1996). The Blind Watchmaker. W.W Norton.
Dorin, A. (2001). Aesthetic Fitness and Artificial Evolution for the Selection of Imagery from the Mythical Infinite Library. In Advances in Artificial Life –Proc. 6th European Conference on Artificial Life, pages 659–668. Springer-Verlag.
Ebert, D.S., Musgrave, F.K., Peachey, D., Perlin, K., and Worley, S. (1998). Texturing andModeling: a Procedural Approach. Academic Press, 2 edition.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley.
Graf, J. and Banzhaf, W. (1995). Interactive Evolution of Images. In Proc. Intl. Conf. on Evolutionary Programming, pages 53–65.
Ibrahim, A.E.M. (1998). GenShade: an Evolutionary Approach to Automatic and Interactive Procedural Texture Generation. PhD thesis, Texas A&M University.
Lewis,M. (2000). Aesthetic Evolutionary Design with Data Flow Networks. In Proc. Generative Art 2000.
Luke, S. (2010). Ecj. Last accessed Feb 24, 2010.
Machado, P. and Cardoso, A. (1998). Computing Aesthetics. In Proc. XIVth Brazilian Symposium on AI, pages 239–249. Springer-Verlag.
Machado, P. and Cardoso, A. (2002). All the Truth About NEvAr. Applied Intelligence, 16(2):101–118.
Neufeld, C.,Ross, B., and Ralph,W. (2008). The Evolution of Artistic Filters. In Romero, J. and Machado, P., editors, The Art of Artificial Evolution. Springer.
Ralph, W. (2006). Painting the Bell Curve: The Occurrence of the Normal Distribution in Fine Art. In preparation.
Romero, J. and Machado, P. (2008). The Art of Artificial Evolution. Springer.
Rooke, S. (2002). Eons of Genetically Evolved Algorithmic Images. In Bentley, P.J. and Corne,D.W., editors,Creative Evolutionary Systems, pages 330–365. Morgan Kaufmann.
Ross, B.J., Ralph, W., and Zong, H. (2006).Evolutionary Image Synthesis Using a Model of Aesthetics. In CEC 2006.
Sims, K. (1993). Interactive evolution of equations for procedural models. The Visual Computer, 9:466–476. http://dx.doi.org/10.1007/BF01888721
Spector, L. and Alpern, A. (1994). Criticism, culture, and the automatic generation of artworks. In Proc. AAAI-94, pages 3–8. AAAI Press/MIT Press.
Svangard, N. and Nordin, P. (2004). Automated Aesthetic Selection of Evolutionary Art by Distance Based Classification of Genomes and Phenomes using the Universal Similarity Metric. In Applications of Evolutionary Computing: EvoWorkshops 2004, pages 447–456. Springer. LNCS 3005.
Todd, S. and Latham, W. (1992). Evolutionary Art and Computers. Academic Press.
Whitelaw,M. (2002). Breeding Aesthetic Objects: Art and Artificial Evolution. In Bentley, P. and Corne,D.W., editors,CreativeEvolutionary Systems, pages 129–145. Morgan Kaufmann.
Wiens, A.L. and Ross, B.J. (2002). Gentropy: Evolutionary 2D Texture Generation. Computers and Graphics Journal, 26(1):75–88. http://dx.doi.org/10.1016/S0097-8493(01)00159-5
Links
Full Text
[extern file]
Sonstige Links
Präsentation: http://www.cosc.brocku.ca/~bross/JNeticTextures/GPTP2010.pptx
http://www.cosc.brocku.ca/~bross/JNeticTextures/paper_images/
