Regressor Survival Rate Estimation for Enhanced Crossover Configuration

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

@InProceedings{Patelli:2011:ICANNGA,

• author = "Alina Patelli and Lavinia Ferariu",
• title = "Regressor Survival Rate Estimation for Enhanced Crossover Configuration",
• year = "2011",
• booktitle = "10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011",
• editor = "Andrej Dobnikar and Uros Lotric and Branko Ster",
• series = "Lecture Notes in Computer Science",
• volume = "6593",
• pages = "290--299",
• address = "Ljubljana, Slovenia",
• month = "14-16 " # apr,
• publisher = "Springer",
• note = "Revised selected papers",
• isbn13 = "978-3-642-20281-0",
• keywords = "genetic algorithms, genetic programming, fuzzy control, schema theory, nonlinear systems identification multiobjective optimisation, building blocks",
• isbn13 = "978-3-642-20281-0",
• DOI = "doi:10.1007/978-3-642-20282-7_30",
• size = "10 pages",
• abstract = "In the framework of nonlinear systems identification by means of multiobjective genetic programming, the paper introduces a customised crossover operator, guided by fuzzy controlled regressor encapsulation. The approach is aimed at achieving a balance between exploration and exploitation by protecting well adapted subtrees from division during recombination. To reveal the benefits of the suggested genetic operator, the authors introduce a novel mathematical formalism which extends the Schema Theory for cut point crossover operating on trees encoding regressor based models. This general framework is afterwards used for monitoring the survival rates of fit encapsulated structural blocks. Other contributions are proposed in answer to the specific requirements of the identification problem, such as a customized tree building mechanism, enhanced elite processing and the hybridisation with a local optimisation procedure. The practical potential of the suggested algorithm is demonstrated in the context of an industrial application involving the identification of a subsection within the sugar factory of Lublin, Poland.",
• notes = "ICANNGA 2011",

}

Genetic Programming entries for Alina Patelli Lavinia Ferariu

Citations