GFS.Thrift: GFS.Thrift model building

Description Usage Arguments Details References See Also


This is the internal function that implements the Thrift's technique based on a genetic algorithm. It is used to tackle regression tasks. Users do not need to call it directly, but just use frbs.learn and predict.


GFS.Thrift(data.train, popu.size = 10, num.labels, persen_cross = 0.6,
  persen_mutant = 0.3, max.gen = 10,, type.defuz = "WAM",
  type.tnorm = "MIN", type.snorm = "MAX", = "TRIANGLE",
  type.implication.func = "ZADEH")



a matrix (m \times n) of normalized data for the training process, where m is the number of instances and n is the number of variables; the last column is the output variable. Note the data must be normalized between 0 and 1.


the size of the population which is generated in each generation.


a matrix describing the number of linguistic terms.


a real number between 0 and 1 representing the probability of crossover.


a real number between 0 and 1 representing the probability of mutation.


the maximal number of generations for the genetic algorithm.

a matrix containing the ranges of the original data.


the type of the defuzzification method. For more detail, see defuzzifier. The default value is WAM.


the type of t-norm. For more detail, please have a look at inference.


the type of s-norm. For more detail, please have a look at inference.

the type of shape of membership function. See fuzzifier.


the type of implication function. See WM.


This method was developed by Thrift using Mamdani's model as fuzzy IF-THEN rules. In this method, we consider a table as a genotype with alleles that are fuzzy set indicators over the output domain. The phenotype is produced by the behavior produced by the fuzzification, max-* composition, and defuzzification operations. A chromosome (genotype) is formed from the decision table by going rowwise and producing a string of numbers from the code set. Standard crossover and mutation operators can act on these string.


P. Thrift, "Fuzzy logic synthesis with genetic algorithms", In Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA91), San Diego (United States of America), pp. 509 - 513 (1991).

See Also

GFS.Thrift.test, frbs.learn, and predict

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