Evolving search spaces to emphasize the performance difference of real-coded crossovers using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Shirakawa:2010:cec,

• author = "Shinichi Shirakawa and Noriko Yata and Tomoharu Nagao",
• title = "Evolving search spaces to emphasize the performance difference of real-coded crossovers using genetic programming",
• booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• isbn13 = "978-1-4244-6910-9",
• abstract = "When we evaluate the search performance of an evolutionary computation (EC) technique, we usually apply it to typical benchmark functions and evaluate its performance in comparison to other techniques. In experiments on limited benchmark functions, it can be difficult to understand the features of each technique. In this paper, the search spaces that emphasise the performance difference of EC techniques are evolved by Cartesian genetic programming. We focus on a real-coded genetic algorithm, which is a type of genetic algorithm that has a real-valued vector as a chromosome. In particular, we generate search spaces using the performance difference of real-coded crossovers. In the experiments, we evolve the search spaces using the combination of three types of real-coded crossovers. As a result of our experiments, the search spaces that exhibit the largest performance difference of two crossovers are generated for all the combinations.",
• DOI = "doi:10.1109/CEC.2010.5586065",
• notes = "WCCI 2010. Also known as \cite{5586065}",

}

Genetic Programming entries for Shinichi Shirakawa Noriko Yata Tomoharu Nagao

Citations