The training set and generalization in grammatical evolution for autonomous agent navigation
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- @Article{Naredo:2017:SC,
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author = "Enrique Naredo and Paulo Urbano and
Leonardo Trujillo",
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title = "The training set and generalization in grammatical
evolution for autonomous agent navigation",
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journal = "Soft Computing",
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year = "2017",
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volume = "21",
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number = "15",
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pages = "4399--4416",
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month = "1 " # aug,
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keywords = "genetic algorithms, genetic programming, Grammatical
evolution, Autonomous agent navigation, Novelty search,
Generalization",
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ISSN = "1433-7479",
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DOI = "doi:10.1007/s00500-016-2072-7",
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size = "18 pages",
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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
scenarios.",
- }
Genetic Programming entries for
Enrique Naredo
Paulo Urbano
Leonardo Trujillo
Citations