Cartesian Ant Programming with adaptive node replacements

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Hara:2014:IWCIA,

• author = "Akira Hara and Jun-ichi Kushida and Keita Fukuhara and Tetsuyuki Takahama",
• booktitle = "7th IEEE International Workshop on Computational Intelligence and Applications (IWCIA 2014)",
• title = "Cartesian Ant Programming with adaptive node replacements",
• year = "2014",
• month = nov,
• pages = "119--124",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ACO, swarm intelligence",
• DOI = "doi:10.1109/IWCIA.2014.6988089",
• ISSN = "1883-3977",
• size = "6 pages",
• abstract = "Ant Colony Optimisation (ACO) is a swarm-based search method. Multiple ant agents search various solutions and their searches focus on around good solutions by positive feedback mechanism based on pheromone communication. ACO is effective for combinatorial optimisation problems. The attempt of applying ACO to automatic programming has been studied in recent years. As one of the attempts, we have previously proposed Cartesian Ant Programming (CAP) as an ant-based automatic programming method. Cartesian Genetic Programming (CGP) is well-known as an evolutionary optimisation method for graph-structural programs. CAP combines graph representations in CGP with pheromone communication in ACO. The connections of program primitives, terminal and functional symbols, can be optimised by ants. CAP showed better performance than CGP. However, quantities of respective symbols are limited due to the fixed assignments of functional symbols to nodes. Therefore, if the number of given nodes is not enough for representing program, the search performance becomes poor. In this paper, to solve the problem, we propose CAP with adaptive node replacements. This method finds unnecessary nodes which are not used for representing programs. Then, new functional symbols, which seems to be useful for constructing good programs, are assigned to the nodes. By this method, given nodes can be used efficiently. In order to examine the effectiveness of our method, we apply it to a symbolic regression problem. CAP with adaptive node replacements showed better results than conventional methods, CGP and CAP.",
• notes = "Also known as \cite{6988089}",

}

Genetic Programming entries for Akira Hara Jun-ichi Kushida Keita Fukuhara Tetsuyuki Takahama

Citations