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)


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.


Novelty Search Grammatical Evolution Genetic Programming 


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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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