Generalization in Maze Navigation Using Grammatical Evolution and Novelty Search

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{conf/tpnc/UrbanoNT14,

• author = "Paulo Urbano and Enrique Naredo and Leonardo Trujillo",
• title = "Generalization in Maze Navigation Using Grammatical Evolution and Novelty Search",
• booktitle = "Third International Conference on Theory and Practice of Natural Computing, TPNC 2014",
• year = "2014",
• editor = "Adrian Horia Dediu and Manuel Lozano and Carlos Martin-Vide",
• volume = "8890",
• series = "Lecture Notes in Computer Science",
• pages = "35--46",
• address = "Granada, Spain",
• month = dec # " 9-11",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, grammatical evolution",
• bibdate = "2014-12-05",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/tpnc/tpnc2014.html#UrbanoNT14",
• isbn13 = "978-3-319-13748-3",
• URL = "https://rdcu.be/dHi36",
• URL = "http://dx.doi.org/10.1007/978-3-319-13749-0",
• DOI = "doi:10.1007/978-3-319-13749-0_4",
• size = "12 pages",
• 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. we compare 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 behaviours that are able to cope with different initial conditions within a static deceptive maze.",
• notes = "LabMAg, Universidade de Lisboa, 1749-016, Lisbon, Portugal",

}

Genetic Programming entries for Paulo Urbano Enrique Naredo Leonardo Trujillo

Citations