Self-tuning geometric semantic Genetic Programming
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- @Article{Castelli:2016:GPEM,
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author = "Mauro Castelli and Luca Manzoni and
Leonardo Vanneschi and Sara Silva and Ales Popovic",
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title = "Self-tuning geometric semantic Genetic Programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2016",
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volume = "17",
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number = "1",
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pages = "55--74",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Semantics,
Parameters Tuning",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-015-9251-7",
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size = "20 pages",
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abstract = "The process of tuning the parameters that characterize
evolutionary algorithms is difficult and can be time
consuming. This paper presents a self-tuning algorithm
for dynamically updating the crossover and mutation
probabilities during a run of genetic programming. The
genetic operators that are considered in this work are
the geometric semantic genetic operators introduced by
Moraglio et al. Differently from other existing
self-tuning algorithms, the proposed one works by
assigning a (different) crossover and mutation
probability to each individual of the population. The
experimental results we present show the
appropriateness of the proposed self-tuning algorithm:
on seven different test problems, the proposed
algorithm finds solutions of a quality that is better
than, or comparable to, the one achieved using the best
known values for the geometric semantic crossover and
mutation rates for the same problems. Also, we study
how the mutation and crossover probabilities change
during the execution of the proposed self-tuning
algorithm, pointing out an interesting insight:
mutation is basically the only operator used in the
exploration phase, while crossover is used for
exploitation, further improving good quality
solutions.",
- }
Genetic Programming entries for
Mauro Castelli
Luca Manzoni
Leonardo Vanneschi
Sara Silva
Ales Popovic
Citations