Improving Grammatical Evolution in Santa Fe Trail using Novelty Search
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Urbano:2013:ECAL,
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author = "Paulo Urbano and Loukas Georgiou",
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title = "Improving Grammatical Evolution in {Santa Fe} Trail
using Novelty Search",
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booktitle = "Advances in Artificial Life, ECAL 2013",
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year = "2013",
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editor = "Pietro Lio and Orazio Miglino and Giuseppe Nicosia and
Stefano Nolfi and Mario Pavone",
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series = "Complex Adaptive Systems",
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pages = "917--924",
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address = "Taormina, Italy",
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month = sep # " 2-6",
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publisher = "MIT Press",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-0-262-31709-2",
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DOI = "doi:10.7551/978-0-262-31709-2-ch137",
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size = "8 pages",
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abstract = "Grammatical Evolution is an evolutionary algorithm
that can evolve complete programs using a Backus Naur
form grammar as a plug-in component to describe the
output language. An important issue of Grammatical
Evolution, and evolutionary computation in general, is
the difficulty in dealing with deceptive problems and
avoid premature convergence to local optima. Novelty
search is a recent technique, which does not use the
standard fitness function of evolutionary algorithms
but follows the gradient of behavioural diversity. It
has been successfully used for solving deceptive
problems mainly in neuro-evolutionary robotics where it
was originated. This work presents the first
application of Novelty Search in Grammatical Evolution
(as the search component of the later) and benchmarks
this novel approach in a well known deceptive problem,
the Santa Fe Trail. For the experiments, two grammars
are used: one that defines a search space semantically
equivalent to the original Santa Fe Trail problem as
defined by Koza and a second one which were widely used
in the Grammatical Evolution literature, but which
defines a biased search space. The application of
novelty search requires to characterise behaviour,
using behaviour descriptors and compare descriptions
using behaviour similarity metrics. The conducted
experiments compare the performance of standard
Grammatical Evolution and its Novelty Search variation
using four intuitive behaviour descriptors. The
experimental results demonstrate that Grammatical
Evolution with Novelty Search outperforms the
traditional fitness based Grammatical Evolution
algorithm in the Santa Fe Trail problem demonstrating a
higher success rates and better solutions in terms of
the required steps.",
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notes = "jGE Netlogo. http://www.dmi.unict.it/ecal2013/
http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013
ECAL-2013",
- }
Genetic Programming entries for
Paulo Urbano
Loukas Georgiou
Citations