$\lambda$-LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem
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- @Article{journals/kais/SottoMB17,
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title = "{{$\lambda$}-LGP}: an improved version of linear
genetic programming evaluated in the Ant Trail
problem",
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author = "Leo Francoso Dal Piccol Sotto and
Vinicius Veloso {de Melo} and Marcio Porto Basgalupp",
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journal = "Knowledge and Information Systems",
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year = "2017",
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number = "2",
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volume = "52",
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pages = "445--465",
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month = aug,
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keywords = "genetic algorithms, genetic programming, ant trail
problem, linear genetic programming, automatic design
of algorithms",
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ISSN = "0219-1377",
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bibdate = "2017-07-26",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/kais/kais52.html#SottoMB17",
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DOI = "doi:10.1007/s10115-016-1016-y",
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abstract = "The Ant Trail problem has been widely investigated as
a benchmark for automatic design of algorithms. One
must design the program of a virtual ant to collect all
pieces of food located in different places of a map,
which may have obstacles, in a predefined limit of
steps. This is a challenging problem, but several
evolutionary computation (EC) researchers have reported
methods with good results. In this paper, we propose an
EC method called l-linear genetic programming
(lambda-LGP), a variation of the well-known linear
genetic programming (LGP) algorithm. Starting with an
LGP based only on effective macro- and micro-mutations,
the lambda-LGP proposed in this work consists in
extending how the individuals are chosen for
reproduction. In this model, a number () of mutations
is applied to each individual, trying to explore its
neighbouring fitness regions; such individual might be
replaced by one of its children according to different
criteria. Several configurations were tested over three
different trails: the Santa Fe, the Los Altos Hill, and
the John Muir. Results show a very significant
improvement over LGP by using this proposed variation.
Also, lambda-LGP outperformed not only LGP, but also
other state-of-the-art methods from the literature.",
- }
Genetic Programming entries for
Leo Francoso Dal Piccol Sotto
Vinicius Veloso de Melo
Marcio Porto Basgalupp
Citations