Evolving Evolutionary Algorithms Using Multi Expression Programming

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

@InProceedings{oltean:2003:ECAL,

• author = "Mihai Oltean and Crina Grosan",
• title = "Evolving Evolutionary Algorithms Using Multi Expression Programming",
• booktitle = "Advances in Artificial Life. 7th European Conference on Artificial Life",
• year = "2003",
• editor = "Wolfgang Banzhaf and Thomas Christaller and Peter Dittrich and Jan T. Kim and Jens Ziegler",
• number = "2801",
• series = "Lecture Notes in Artificial Intelligence",
• pages = "651--658",
• address = "Dortmund, Germany",
• month = "14-17 " # sep,
• publisher = "Springer",
• email = "moltean@cs.ubbcluj.ro",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-20057-6",
• URL = "http://www.mep.cs.ubbcluj.ro/oltean_ecal2003.pdf",
• DOI = "doi:10.1007/b12035",
• size = "8 pages",
• abstract = "Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is used. Each MEP chromosome will encode multiple EAs. An nongenerational EA for function optimisation is evolved in this paper. Numerical experiments show the effectiveness of this approach.",
• notes = "ECAL-2003 Also available at www.eea.cs.ubbcluj.ro",

}

Genetic Programming entries for Mihai Oltean Crina Grosan

Citations