Genetic Programming with Linear Representation: a Survey

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@Article{journals/ijait/OlteanGDM09,

• title = "Genetic Programming with Linear Representation: a Survey",
• author = "Mihai Oltean and Crina Grosan and Laura Diosan and Cristina Mihaila",
• journal = "International Journal on Artificial Intelligence Tools",
• year = "2009",
• number = "2",
• volume = "18",
• pages = "197--238",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, linear genetic programming, gene expression programming, multi expression programming, grammatical evolution, stack-based genetic programming",
• DOI = "doi:10.1142/S0218213009000111",
• bibdate = "2009-09-23",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijait/ijait18.html#OlteanGDM09",
• abstract = "Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.",

}

Genetic Programming entries for Mihai Oltean Crina Grosan Laura Diosan Cristina Mihaila

Citations