Adaptive genetic programming applied to classification in data mining
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Al-Madi:2012:NaBIC,
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author = "N. Al-Madi and S. A. Ludwig",
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booktitle = "Proceedings of the Fourth World Congress on Nature and
Biologically Inspired Computing, NaBIC 2012",
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title = "Adaptive genetic programming applied to classification
in data mining",
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year = "2012",
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pages = "79--85",
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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",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Adaptive_Genetic_Programming_applied_to_Classification_in_Data_Mining.pdf",
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DOI = "doi:10.1109/NaBIC.2012.6402243",
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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.",
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notes = "Also known as \cite{6402243}",
- }
Genetic Programming entries for
Nailah Al-Madi
Simone A Ludwig
Citations