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Maintaining Population Diversity in Evolutionary Art

  • E. den Heijer
  • A. E. Eiben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7247)

Abstract

Evolutionary art is inherently more concerned with exploration than with exploitation, because users are typically more interested in evolving a collection of diverse images than converging to a single ‘optimal’ image. However, maintaining diversity is a difficult task. In this paper we investigate various techniques to promote population diversity in evolutionary art. We introduce customised mutation and crossover operators that perform a local search to diversify individuals and evaluate the effect of these operators on population diversity. We also investigate alternatives for the fitness crowding operator in NSGA-II; we use a genotype and a phenotype distance function to calculate the crowding distance and investigate their effect on population diversity.

Keywords

Local Search Distance Function Genetic Program Pareto Front Population Diversity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    del Acebo, E., Sbert, M.: Benford’s law for natural and synthetic images. In: Neumann et al. [17], pp. 169–176Google Scholar
  2. 2.
    Bergen, S., Ross, B.J.: Evolutionary art using summed multi-objective ranks. In: Riolo, R., McConaghy, T., Vladislavleva, E. (eds.) Genetic Programming Theory and Practice VIII, Genetic and Evolutionary Computation, vol. 8, pp. 227–244. Springer, New York (2011)CrossRefGoogle Scholar
  3. 3.
    Boden, M.: The Creative Mind. Abacus (1990)Google Scholar
  4. 4.
    Boden, M.: Creativity and Art: Three Roads to Surprise. Oxford University Press (2010)Google Scholar
  5. 5.
    Burke, E., Gustafson, S., Kendall, G., Krasnogor, N.: Advanced Population Diversity Measures in Genetic Programming. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN VII. LNCS, vol. 2439, pp. 341–350. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation 8(1), 47–62 (2004)CrossRefGoogle Scholar
  7. 7.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  8. 8.
    Eiben, A., Schippers, A.: On evolutionary exploration and exploitation. Fundamenta Informaticae 35(1-4), 35–50 (1998)zbMATHGoogle Scholar
  9. 9.
    Ekárt, A., Németh, S.: A Metric for Genetic Programs and Fitness Sharing. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 259–270. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    den Heijer, E., Eiben, A.E.: Using aesthetic measures to evolve art. In: IEEE Congress on Evolutionary Computation (CEC 2010), July 18-23, IEEE Press, Barcelona (2010)Google Scholar
  11. 11.
    den Heijer, E., Eiben, A.: Comparing Aesthetic Measures for Evolutionary Art. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    den Heijer, E., Eiben, A.: Evolving Art Using Multiple Aesthetic Measures. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Drechsler, R., Farooq, M., Grahl, J., Greenfield, G., Prins, C., Romero, J., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Urquhart, N., Uyar, A.Ş. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 234–243. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Jackson, D.: Phenotypic Diversity in Initial Genetic Programming Populations. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 98–109. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Jackson, D.: Promoting Phenotypic Diversity in Genetic Programming. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 472–481. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  16. 16.
    Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W.: Global contrast factor-a new approach to image contrast. In: Neumann et al. [17], pp. 159–168Google Scholar
  17. 17.
    Neumann, L., Sbert, M., Gooch, B., Purgathofer, W. (eds.): Computational Aesthetics 2005: Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging 2005, Girona, Spain, May 18-20. Eurographics Association (2005)Google Scholar
  18. 18.
    Nguyen, T.H., Nguyen, X.H.: A brief overview of population diversity measures in genetic programming. In: Pham, T.L., Le, H.K., Nguyen, X.H. (eds.) Proceedings of the Third Asian-Pacific Workshop on Genetic Programming, pp. 128–139 (2006)Google Scholar
  19. 19.
    Ross, B., Ralph, W., Zong, H.: Evolutionary image synthesis using a model of aesthetics. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1087–1094 (2006)Google Scholar
  20. 20.
    Stricker, M., Orengo, M.: Similarity of color images. Storage and Retrieval of Image and Video Databases III 2, 381–392 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • E. den Heijer
    • 1
    • 2
  • A. E. Eiben
    • 2
  1. 1.Objectivation B.V.AmsterdamThe Netherlands
  2. 2.Vrije UniversiteitAmsterdamThe Netherlands

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