Evaluating the Effects of Local Search in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Z-Flores:2014:EVOLVE,
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author = "Emigdio Z-Flores and Leonardo Trujillo and
Oliver Schuetze and Pierrick Legrand",
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title = "Evaluating the Effects of Local Search in Genetic
Programming",
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booktitle = "EVOLVE - A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation V",
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year = "2014",
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editor = "Alexandru-Adrian Tantar and Emilia Tantar and
Jian-Qiao Sun and Wei Zhang and Qian Ding and
Oliver Schuetze and Michael Emmerich and Pierrick Legrand and
Pierre {Del Moral} and Carlos A. {Coello Coello}",
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volume = "288",
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series = "Advances in Intelligent Systems and Computing",
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pages = "213--228",
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address = "Peking",
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month = "1-4 " # jul,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Local Search,
Memetic Algorithms",
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isbn13 = "978-3-319-07493-1",
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oai = "oai:HAL:hal-01060315v1",
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URL = "https://hal.inria.fr/hal-01060315",
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DOI = "doi:10.1007/978-3-319-07494-8_15",
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abstract = "Genetic programming (GP) is an evolutionary
computation paradigm for the automatic induction of
syntactic expressions. In general, GP performs an
evolutionary search within the space of possible
program syntaxes, for the expression that best solves a
given problem. The most common application domain for
GP is symbolic regression, where the goal is to find
the syntactic expression that best fits a given set of
training data. However, canonical GP only employs a
syntactic search, thus it is intrinsically unable to
efficiently adjust the (implicit) parameters of a
particular expression. This work studies a Lamarckian
memetic GP, that incorporates a local search (LS)
strategy to refine GP individuals expressed as syntax
trees. In particular, a simple parametrisation for GP
trees is proposed, and different heuristic methods are
tested to determine which individuals should be subject
to a LS, tested over several benchmark and real-world
problems. The experimental results provide necessary
insights in this insufficiently studied aspect of GP,
suggesting promising directions for future work aimed
at developing new memetic GP systems.",
- }
Genetic Programming entries for
Emigdio Z-Flores
Leonardo Trujillo
Oliver Schuetze
Pierrick Legrand
Citations