Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming

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@InProceedings{cordon:ppsn2002:pp710,

• author = "Oscar Cordon and Enrique Herrera-Viedma and Maria Luque",
• title = "Evolutionary Learning of {Boolean} Queries by Multiobjective Genetic Programming",
• booktitle = "Parallel Problem Solving from Nature - PPSN VII",
• address = "Granada, Spain",
• month = "7-11 " # sep,
• pages = "710--719",
• year = "2002",
• editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel",
• number = "2439",
• series = "Lecture Notes in Computer Science, LNCS",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, MOGA, Pattern recognition and classification/datamining,Web services, Multi-objective",
• ISBN = "3-540-44139-5",
• DOI = "doi:10.1007/3-540-45712-7_68",
• abstract = "The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimisation of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a previous proposal of a genetic programming technique for this task.",

}

Genetic Programming entries for Oscar Cordon Enrique Herrera Viedma Maria Luque Rodriguez

Citations