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- Title
$${\lambda }$$ -LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem.
- Authors
Sotto, Léo; Melo, Vinícius; Basgalupp, Márcio
- 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 $${\lambda }$$ -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 ( $${\lambda }$$ ) of mutations is applied to each individual, trying to explore its neighboring 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.
- Subjects
GENETIC programming; EVOLUTIONARY computation; COMPUTER algorithms; GENETIC mutation; GENETICS; BIOLOGICAL fitness
- Publication
Knowledge & Information Systems, 2017, Vol 52, Issue 2, p445
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-016-1016-y