$\lambda$-LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem

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@Article{journals/kais/SottoMB17,

• title = "{{$\lambda$}-LGP}: an improved version of linear genetic programming evaluated in the Ant Trail problem",
• author = "Leo Francoso Dal Piccol Sotto and Vinicius Veloso {de Melo} and Marcio Porto Basgalupp",
• journal = "Knowledge and Information Systems",
• year = "2017",
• number = "2",
• volume = "52",
• pages = "445--465",
• month = aug,
• keywords = "genetic algorithms, genetic programming, ant trail problem, linear genetic programming, automatic design of algorithms",
• ISSN = "0219-1377",
• bibdate = "2017-07-26",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kais/kais52.html#SottoMB17",
• DOI = "doi:10.1007/s10115-016-1016-y",
• 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