Controlling the Population Size in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{sbia2002meta033,

• title = "Controlling the Population Size in Genetic Programming",
• author = "Eduardo Spinosa and Aurora Pozo",
• year = "2002",
• identifier = "sbia2002article033",
• language = "eng",
• rights = "Sociedade Brasileira de Computa{\c c}{\~a}o",
• source = "sbia2002",
• booktitle = "Advances in Artificial Intelligence: 16th Brazilian Symposium on Artificial Intelligence, SBIA 2002",
• editor = "G. Bittencourt and G .L. Ramalho",
• volume = "2507",
• series = "Lecture Notes in Computer Science",
• pages = "345--354",
• address = "Porto de Galinhas, Recife, Brasil",
• month = "11-14 " # nov,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/3-540-36127-8_33",
• abstract = "Evolutionary Computation (EC) introduces a new paradigm for solving problems in Artificial Intelligence, representing solution candidates as individuals and evolving them based on Darwin's Theory of Natural Selection. Genetic Algorithms (GA) and Genetic Programming (GP), two important EC techniques, have been successfully applied both in theoretical scenarios and practical situations. This work discusses an issue of great relevance and impact on this type of algorithm: the automatic adjustment of the parameters that control the search process. Based on a recent research, a method that controls the population size in a GA is adapted and implemented in GP. A series of classic experiments has been performed before and after the modifications, showing that this method can improve the algorithms' robustness and reliability. The data allow a discussion about the method and the importance of the adaptation of parameters in EC algorithms.",

}

Genetic Programming entries for Eduardo J Spinosa Aurora Trinidad Ramirez Pozo

Citations