A (mu + lambda) - GP Algorithm and its use for Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Costa:2006:ICTAI,

• author = "Eduardo Oliveira Costa and Aurora Pozo",
• title = "A (mu + lambda) - {GP} Algorithm and its use for Regression Problems",
• booktitle = "8th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '06",
• year = "2006",
• pages = "10--17",
• address = "Arlington, VA, USA",
• month = "13-15 " # nov,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7695-2728-0",
• DOI = "doi:10.1109/ICTAI.2006.6",
• size = "8 pages",
• abstract = "The genetic programming (GP) is a powerful technique for symbolic regression. However, because it is a new area, many improvements can be obtained changing the basic behaviour of the method. In this way, this work develop a different genetic programming algorithm doing some modifications on the classical GP algorithm and adding some concepts of evolution strategies. The new approach was evaluated using two instances of symbolic regression problem - the binomial-3 problem (a tunably difficult problem), proposed in (J.M. Daida et al., 2001) and the problem of modelling software reliability growth (an application of symbolic regression). The discovered results were compared with the classical GP algorithm. The symbolic regression problems obtained excellent results and an improvement was detected using the proposed approach",
• notes = "Dept. of Comput. Sci., Fed. Univ. of Parana, Curitiba",

}

Genetic Programming entries for Eduardo Oliveira Costa Aurora Trinidad Ramirez Pozo

Citations