Semantic Operators for Evolutionary Art

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Joao Correia and Penousal Machado: Semantic Operators for Evolutionary Art. In: Semantic Methods in Genetic Programming, 13 September 2014, Workshop at Parallel Problem Solving from Nature 2014 conference



Semantic aware crossover and mutation operators [5] have become a hot topic of research within the Genetic Programming (GP) community. Inspired on the works of Sims [4] and Hart [1] concerning the generation of short animations through “genetic morphing” between individuals we propose semantic aware operators for evolutionary art.

Extended Abstract


Used References

[1] D. A. Hart. Toward greater artistic control for interactive evolution of images and animation. In Proceedings of the 2007 EvoWorkshops, pages 527–536, Berlin, Heidelberg, 2007. Springer-Verlag.

[2] T. Jiang, L. Wang, and K. Zhang. Alignment of trees – an alternative to tree edit. Theoretical Computer Science, 143(1):137–148, 1995.

[3] A. Moraglio, K. Krawiec, and C. G. Johnson. Geometric semantic genetic programming. In C. A. C. Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, and M. Pavone, editors, PPSN (1), volume 7491 of Lecture Notes in Com- puter Science, pages 21–31. Springer, 2012.

[4] K. Sims. Artificial evolution for computer graphics. ACM Computer Graph- ics, 25:319–328, 1991.

[5] N. Q. Uy, N. X. Hoai, M. O’Neill, R. I. McKay, and E. G. L ́opez. Semantically-based crossover in genetic programming: application to real- valued symbolic regression. Genetic Programming and Evolvable Machines, 12(2):91–119, 2011.

[6] L. Vanneschi, M. Castelli, L. Manzoni, and S. Silva. A new implementation of geometric semantic gp and its application to problems in pharmacokinetics. In K. Krawiec, A. Moraglio, T. Hu, A. S. Etaner-Uyar, and B. Hu, editors, EuroGP, volume 7831 of Lecture Notes in Computer Science, pages 205–216. Springer, 2013.


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