A Comparison Between Representations for Evolving Images
Inhaltsverzeichnis
Referenz
Alessandro Re, Mauro Castelli, Leonardo Vanneschi: A Comparison Between Representations for Evolving Images. In: EvoMUSART 2016, 163-185.
DOI
http://dx.doi.org/10.1007/978-3-319-31008-4_12
Abstract
Evolving images using genetic programming is a complex task and the representation of the solutions has an important impact on the performance of the system. In this paper, we present two novel representations for evolving images with genetic programming. Both these representations are based on the idea of recursively partitioning the space of an image. This idea distinguishes these representations from the ones that are currently most used in the literature. The first representation that we introduce partitions the space using rectangles, while the second one partitions using triangles. These two representations are compared to one of the most well known and frequently used expression-based representations, on five different test cases. The presented results clearly indicate the appropriateness of the proposed representations for evolving images. Also, we give experimental evidence of the fact that the proposed representations have a higher locality compared to the compared expression-based representation.
Extended Abstract
Bibtex
@incollection{
year={2016},
isbn={978-3-319-31007-7},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={9596},
series={Lecture Notes in Computer Science},
editor={Johnson, Colin and Ciesielski, Vic and Correia, João and Machado, Penousal},
doi={http://dx.doi.org/10.1007/978-3-319-16498-4_12},
title={A Comparison Between Representations for Evolving Images},
url={http://link.springer.com/chapter/10.1007/978-3-319-31008-4_12 http://de.evo-art.org/index.php?title=A_Comparison_Between_Representations_for_Evolving_Images },
publisher={Springer International Publishing},
keywords={Genetic programming (GP), Image representation, Locality},
author={Re, Alessandro and Castelli, Mauro and Vanneschi, Leonardo},
pages={163-185},
language={English}
}
Used References
1. Draves, Scott: The Electric Sheep Screen-Saver: A Case Study in Aesthetic Evolution. In: EvoMUSART 2005, 458-467. DOI: http://link.springer.com/10.1007/978-3-540-32003-6_46 http://draves.org/evomusart05/evomusart05draves.pdf
2. Hart, David Augustus: Toward Greater Artistic Control for Interactive Evolution of Images and Animation. In: EvoMUSART 2007, 527-536. DOI: http://link.springer.com/10.1007/978-3-540-71805-5_58 http://dahart.com/paper/hart_evomusart_2007_paper.pdf
3. Juan Romero, Penousal Machado (Herausgeber): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. 1 Aufl., Springer, Berlin, 2007, ISBN 978-3540728764, 460 Seiten. DOI: http://link.springer.com/10.1007/978-3-540-72877-1
4. World, L.: Aesthetic selection: The evolutionary art of Steven Rooke. IEEE Computer Graphics and Applications 16(1) (1996) DOI: http://dx.doi.org/10.1109/MCG.1996.481558 (no references)
5. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002) http://dx.doi.org/10.1007/978-3-662-04726-2
6. Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)MATH
7. Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation 19(2), 189–223 (2011). DOI: 10.1162/EVCO_a_00025 http://www.cs.swarthmore.edu/~meeden/DevelopmentalRobotics/lehman_ecj11.pdf
8. Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connection Science 6, 325–354 (1994) DOI: http://dx.doi.org/10.1080/09540099408915729 https://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf
9. Nguyen, A., Yosinski, J., Clune, J.: Innovation engines: automated creativity and improved stochastic optimization via deep learning. In: Proceedings of the Genetic and Evolutionary Computation Conference (2015) http://dx.doi.org/10.1145/2739480.2754703 http://www.evolvingai.org/files/InnovationEngine_gecco15.pdf
10. Joao Correia, Penousal Machado, Juan Romero, and Adrián Carballal: Evolving Figurative Images Using Expression-Based Evolutionary Art. In: Computational Creativity 2013 ICCC 2013. pp. 24-31. http://www.computationalcreativity.net/iccc2013/download/iccc2013-correia-et-al.pdf http://fmachado.dei.uc.pt/wp-content/papercite-data/pdf/cmrc13b.pdf
11. Penousal Machado, João Correia, Juan Romero: Expression-Based Evolution of Faces. In: EvoMUSART 2012, 187-198. DOI: http://link.springer.com/10.1007/978-3-642-29142-5_17 http://fmachado.dei.uc.pt/wp-content/papercite-data/pdf/mjc12a.pdf
12. Tiago Martins, João Correia, Ernesto Costa, Penousal Machado: Evotype: Evolutionary Type Design. In: EvoMUSART 2015, 136-147. DOI: http://link.springer.com/chapter/10.1007/978-3-319-16498-4_13 http://www.researchgate.net/publication/280308590_Evotype_Evolutionary_Type_Design
13. Woolley, B.G., Stanley, K.O.: On the deleterious effects of a priori objectives on evolution and representation. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 957–964. ACM (2011)
14. Galván-López, E., McDermott, J., O’Neill, M., Brabazon, A.: Defining locality as a problem difficulty measure in genetic programming. Genet. Program Evolvable Mach. 12(4), 365–401 (2011) http://dx.doi.org/10.1007/s10710-011-9136-3
15. Galvan, E., Trujillo, L., McDermott, J., Kattan, A.: Locality in continuous fitness-valued cases and genetic programming difficulty. In: Schütze, O., Coello Coello, C.A., Tantar, A.-A., Tantar, E., Bouvry, P., Del Moral, P., Legrand, P. (eds.) EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation II. AISC, vol. 175, pp. 41–56. Springer, Heidelberg (2012) http://dx.doi.org/10.1007/978-3-642-31519-0_3
16. Stadler, P.F.: Fitness landscapes. In: Biological Evolution and Statistical Physics, pp. 183–204. Springer, Heidelberg (2002) http://dx.doi.org/10.1007/3-540-45692-9_10 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.5364 https://www.bioinf.uni-leipzig.de/~studla/Publications/PREPRINTS/01-pfs-004.pdf
17. Rothlauf, F.: Design of representations and search operators. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 1061–1083. Springer, Heidelberg (2015) http://dx.doi.org/10.1007/978-3-662-43505-2_53
18. Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, D. (ed.) FOGA-2, pp. 93–108. Morgan Kaufmann, San Francisco (1993)
19. Horn, J., Goldberg, D.E.: Genetic algorithm difficulty and the modality of the fitness landscapes. In: Whitley, D., Vose, M. (eds.) FOGA-3, pp. 243–269. Morgan Kaufmann, San Francisco (1995)
20. Mitchell, M., Forrest, S., Holland, J.: The royal road for genetic algorithms: fitness landscapes and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pp. 245–254. MIT Press, Cambridge (1996)
21. Forrest, S., Mitchell, M.: What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Mach. Learn. 13, 285–319 (1993) http://dx.doi.org/10.1023/A%3A1022626114466
22. Chakraborty, U.K., Janikow, C.Z.: An analysis of gray versus binary encoding in genetic search. Inf. Sci. 156(3–4), 253–269 (2003) http://dx.doi.org/10.1016/S0020-0255(03)00178-6
23. Vanneschi, L.: Theory and Practice for Efficient Genetic Programming. Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland (2004)
24. Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, B.: The role of syntactic and semantic locality of crossover in genetic programming. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 533–542. Springer, Heidelberg (2010)
25. Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, R., Phong, D.N.: On the roles of semantic locality of crossover in genetic programming. Inf. Sci. 235, 195–213 (2013). Data-based Control. Decision, Scheduling and Fault Diagnostics http://dx.doi.org/10.1016/j.ins.2013.02.008
26. Eelco den Heijer, A. E. Eiben: Evolving Pop Art Using Scalable Vector Graphics. In: EvoMUSART 2012, 48-59. DOI: http://link.springer.com/10.1007/978-3-642-29142-5_5
27. den Heijer, E., Eiben, A.: Evolving art with scalable vector graphics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 427–434. ACM (2011) DOI: http://dx.doi.org/10.1145/2001576.2001635 http://www.few.vu.nl/~eelco/publications/E-den-Heijer-and-AE-Eiben-Evolving-Art-With-Scalable-Vector-Graphics-2011.pdf
28. Baker, E., Seltzer, M.I.: Evolving Line Drawings. In: Graphics Interface 1994, Banff, Canada, pp. 91–100 (1994). http://www.eecs.harvard.edu/margo/papers/graphics94/ http://www.eecs.harvard.edu/margo/papers/graphics94/paper.pdf
29. Tatsuo Unemi: An IEC-based support system for font design. In: 2003 IEEE International Conference on Systems, Man and Cybernetics, 968–973. DOI: http://dx.doi.org/10.1109/ICSMC.2003.1243940
30. Michael Schmitz (2004). GenoTyp, an experiment about genetic typography. In: Generative Art 2004. http://www.generativeart.com/on/cic/papersGA2004/b27.htm
31. Pagliarini, L., Parisi, D.: Face-it project. In: Proceedings of XV Italian Congress on Experimental Psychology, pp. 38–41 (1996)
32. Alsing, R.: Genetic programming: Evolution of Mona Lisa (2008). http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/
33. Karl Sims: Artificial evolution for computer graphics. In: SIGGRAPH 1991 Proceedings, vol. 25, pp. 319–328. ACM, New York (1991), http://dx.doi.org/10.1145/122718.122752 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.2547 http://www.karlsims.com/papers/siggraph91.html http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.2547&rep=rep1&type=pdf
34. Lewis, Matthew: Evolutionary Visual Art and Design. In: Juan Romero; Penousal Machado: The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Springer, Berlin, 2007, 3-37. DOI: http://link.springer.com/10.1007/978-3-540-72877-1_1 http://web.info.uvt.ro/~dzaharie/cne2013/proiecte/aplicatii/EvolutionaryArt/EvolutionaryDesign.pdf
35. Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program Evolvable Mach. 8(2), 131–162 (2007) http://dx.doi.org/10.1007/s10710-007-9028-8
36. Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011). DOI: http://dx.doi.org/10.1162/EVCO_a_00030 http://eplex.cs.ucf.edu/papers/secretan_ecj11.pdf
Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011). DOI: http://dx.doi.org/10.1162/EVCO_a_00030 http://eplex.cs.ucf.edu/papers/secretan_ecj11.pdf
37. Jon McCormack: Open Problems in Evolutionary Music and Art. In: EvoMUSART 2005, 428-436. DOI: http://link.springer.com/10.1007/978-3-540-32003-6_43 http://www.csse.monash.edu.au/~jonmc/research/Papers/OpenProblemsSV.pdf
38. Jiarathanakul, P.: Ray marching distance fields in real-time on webgl. Technical report, Citeseer
39. Quilez, I.: Modeling with distance functions (2008). http://iquilezles.org/www/articles/distfunctions/distfunctions.htm
40. Gary Greenfield: Mathematical building blocks for evolving expressions. In: Bridges 2000. Pages 61–70 http://archive.bridgesmathart.org/2000/bridges2000-61.html http://archive.bridgesmathart.org/2000/bridges2000-61.pdf
41. Ventrella, Jeffrey: Evolving the Mandelbrot Set to Imitate Figurative Art. In: Hingston & Barone & Michalewicz: Design by Evolution, Springer, Berlin, 2008. 145-168. DOI: http://link.springer.com/10.1007/978-3-540-74111-4_9 http://www.ventrella.com/Tweaks/Portraits/EvolvingMandelbrot.pdf
42. Lutton, Evelyne, Cayla, Emmanuel; Chapuis, Jonathan: ArtiE-Fract: The Artist’s Viewpoint. In: EvoMUSART 2003, 547-558. DOI: http://link.springer.com/10.1007/3-540-36605-9_47 http://evelyne.lutton.free.fr/Papers/134_lutton.pdf
43. Jürgen Schmidhuber: Low-complexity art. Leonardo, 97–103 (1997). JSTOR http://people.idsia.ch/~juergen/beauty.html ftp://ftp.idsia.ch/pub/juergen/locoart.ps.gz http://people.idsia.ch/~juergen/locoart/locoart.html
44. Penousal Machado, Amílcar Cardoso: All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems 16(2), 101–119 (2002). DOI: http://link.springer.com/article/10.1023%2FA%3A1013662402341 http://fmachado.dei.uc.pt/wp-content/papercite-data/pdf/mc02.pdf
45. Di Gesu, V., Starovoitov, V.: Distance-based functions for image comparison. Pattern Recogn. Lett. 20(2), 207–214 (1999) http://dx.doi.org/10.1016/S0167-8655(98)00115-9 http://uiip.bas-net.by/structure/l_ori/starovoitov/Starovoitov_Publication_section/4-Distance-Based%20Functions%20for%20Image%20Comparison-Starovoitov99.pdf
46. D’Agostino, R.B.: An omnibus test of normality for moderate and large size samples. Biometrika 58(2), 341–348 (1971) http://dx.doi.org/10.1093/biomet/58.2.341
47. D’Agostino, R.B., Pearson, E.S.: Tests for departure from normality. empirical results for the distributions of b2 and b1. Biometrika 60(3), 613–622 (1973)
Links
Full Text
