Adaptive genetic programming applied to classification in data mining

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

@InProceedings{Al-Madi:2012:NaBIC,

• author = "N. Al-Madi and S. A. Ludwig",
• booktitle = "Proceedings of the Fourth World Congress on Nature and Biologically Inspired Computing, NaBIC 2012",
• title = "Adaptive genetic programming applied to classification in data mining",
• year = "2012",
• pages = "79--85",
• keywords = "genetic algorithms, genetic programming, data mining, pattern classification, adaptive GP, adaptive genetic programming, classification accuracies, crossover rates, data mining, mutation rates, Accuracy, Evolutionary computation, Sociology, Standards, Statistics, Adaptive Genetic Programming, Classification, Evolutionary Computation",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Adaptive_Genetic_Programming_applied_to_Classification_in_Data_Mining.pdf",
• DOI = "doi:10.1109/NaBIC.2012.6402243",
• abstract = "Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems, however, it suffers from a long run time. In addition, there are many parameters that need to be set before the GP is run. In this paper, we propose an adaptive GP that automatically determines the best parameters of a run, and executes the classification faster than standard GP. This adaptive GP has three variations. The first variant consists of an adaptive selection process ensuring that the produced solutions in the next generation are better than the solutions in the previous generation. The second variant adapts the crossover and mutation rates by modifying the probabilities ensuring that a solution with a high fitness is protected. And the third variant is an adaptive function list that automatically changes the functions used by deleting the functions that do not favourably contribute to the classification. These proposed variations were implemented and compared to the standard GP. The results show that a significant speedup can be achieved by obtaining similar classification accuracies.",
• notes = "Also known as \cite{6402243}",

}

Genetic Programming entries for Nailah Al-Madi Simone A Ludwig

Citations