The training set and generalization in grammatical evolution for autonomous agent navigation

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  author =       "Enrique Naredo and Paulo Urbano and 
                 Leonardo Trujillo",
  title =        "The training set and generalization in grammatical
                 evolution for autonomous agent navigation",
  journal =      "Soft Computing",
  year =         "2017",
  volume =       "21",
  number =       "15",
  pages =        "4399--4416",
  month =        "1 " # aug,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Autonomous agent navigation, Novelty search,
  ISSN =         "1433-7479",
  DOI =          "doi:10.1007/s00500-016-2072-7",
  size =         "18 pages",
  abstract =     "Over recent years, evolutionary computation research
                 has begun to emphasize the issue of generalization.
                 Instead of evolving solutions that are optimized for a
                 particular problem instance, the goal is to evolve
                 solutions that can generalize to various different
                 scenarios. This paper compares objective-based search
                 and novelty search on a set of generalization oriented
                 experiments for a navigation task using grammatical
                 evolution (GE). In particular, this paper studies the
                 impact that the training set has on the generalization
                 of evolved solutions, considering: (1) the training set
                 size; (2) the manner in which the training set is
                 chosen (random or manual); and (3) if the training set
                 is fixed throughout the run or dynamically changed
                 every generation. Experimental results suggest that
                 novelty search outperforms objective-based search in
                 terms of evolving navigation behaviours that are able
                 to cope with different initial conditions. The
                 traditional objective-based search requires larger
                 training sets and its performance degrades when the
                 training set is not fixed. On the other hand, novelty
                 search seems to be robust to different training sets,
                 finding general solutions in almost all of the studied
                 conditions with almost perfect generalization in many

Genetic Programming entries for Enrique Naredo Paulo Urbano Leonardo Trujillo