Generalization in Maze Navigation Using Grammatical Evolution and Novelty Search

  • Paulo Urbano
  • Enrique Naredo
  • Leonardo Trujillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8890)

Abstract

Recent research on evolutionary algorithms has begun to focus on the issue of generalization. While most works emphasize the evolution of high quality solutions for particular problem instances, others are addressing the issue of evolving solutions that can generalize in different scenarios, which is also the focus of the present paper. In particular, this paper compares fitness-based search, Novelty Search (NS), and random search in a set of generalization oriented experiments in a maze navigation problem using Grammatical Evolution (GE), a variant of Genetic Programming. Experimental results suggest that NS outperforms the other search methods in terms of evolving general navigation behaviors that are able to cope with different initial conditions within a static deceptive maze.

Keywords

Novelty Search Grammatical Evolution Genetic Programming 

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References

  1. 1.
    Burke, E.K., Gustafson, S., Kendall, G., Krasnogor, N.: Is Increased Diversity in Genetic Programming Beneficial? An Analysis of Lineage Selection. Ph.D. thesis, University of Nottingham, UK (February 2004)Google Scholar
  2. 2.
    Doucette, J., Heywood, M.: Novelty-based fitness: An evaluation under the santa fe trail. Genetic Programming, 50–61 (2010)Google Scholar
  3. 3.
    Georgiou, L., Teahan, W.J.: jge - a java implementation of grammatical evolution. In: 10th WSEAS International Conference on Systems, Athens, Greece, pp. 534–869 (2006)Google Scholar
  4. 4.
    Georgiou, L., Teahan, W.J.: Grammatical evolution and the santa fe trail problem. In: International Conference on Evolutionary Computation (ICEC), pp. 10–19. SciTePress, Valencia (2010)Google Scholar
  5. 5.
    Gomes, J.C., Urbano, P., Christensen, A.L.: Evolution of swarm robotics systems with novelty search. CoRR abs/1304.3362 (2013)Google Scholar
  6. 6.
    Gonçalves, I., Silva, S.: Experiments on controlling overfitting in genetic programming. In: 15th Portuguese Conference on Artificial Intelligence (EPIA 2011) (2011)Google Scholar
  7. 7.
    Kushchu, I.: Genetic programming and evolutionary generalization. IEEE Transactions on Evolutionary Computation 6(5), 431–442 (2002)CrossRefGoogle Scholar
  8. 8.
    Lehman, J., Stanley, K.: Exploiting open-endedness to solve problems through the search for novelty. In: Bullock, S., Noble, J., Watson, R., Bedau, M.A. (eds.) Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, pp. 329–336. MIT Press, Cambridge (2008)Google Scholar
  9. 9.
    Lehman, J., Stanley, K.O.: Efficiently evolving programs through the search for novelty. In: Pelikan, M., Branke, J. (eds.) GECCO, pp. 837–844. ACM (2010)Google Scholar
  10. 10.
    Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation 19(2), 189–223 (2011)CrossRefGoogle Scholar
  11. 11.
    Li, J., Storie, J., Clune, J.: Encouraging creative thinking in robots improves their ability to solve challenging problems. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO 2014, pp. 193–200. ACM (2014)Google Scholar
  12. 12.
    Mouret, J.B., Doncieux, S.: Encouraging behavioral diversity in evolutionary robotics: An empirical study. Evolutionary Computation 20(1), 91–133 (2012)CrossRefGoogle Scholar
  13. 13.
    Naredo, E., Trujillo, L.: Searching for novel clustering programs. In: GECCO, pp. 1093–1100 (2013)Google Scholar
  14. 14.
    Naredo, E., Trujillo, L., Martínez, Y.: Searching for novel classifiers. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 145–156. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Nicoară, E.S.: Mechanisms to avoid the premature convergence of genetic algorithms. Petroleum - Gas University of Ploiesti Bulletin, Mathematics LXI(1) (2009)Google Scholar
  16. 16.
    O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evolutionary Computation 5(4), 349–358 (2001)CrossRefGoogle Scholar
  17. 17.
    Urbano, P., Loukas, G.: Improving grammatical evolution in santa fe trail using novelty search. In: Advances in Artificial Life, ECAL, pp. 917–924 (2013)Google Scholar
  18. 18.
    Velez, R., Clune, J.: Novelty search creates robots with general skills for exploration. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO 2014, pp. 737–744. ACM (2014)Google Scholar
  19. 19.
    Wilensky, U.: Netlogo, Evanston, IL: Center for Connected Learning and Computer-Based Modeling (1999), http://ccl.northwestern.edu/netlogo

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Paulo Urbano
    • 1
  • Enrique Naredo
    • 2
  • Leonardo Trujillo
    • 2
  1. 1.LabMAgUniversidade de LisboaLisbonPortugal
  2. 2.Tree-LabInstituto Tecnológico de Tijuana, Calz. del Tecnológico S/N, Tomás AquinoTijuanaMéxico

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