# FH.GBML model building

### Description

This is the internal function that implements the Ishibuchi's method based on
hybridization of genetic cooperative-competitive learning (GCCL) and Pittsburgh (FH.GBML). It is used to solve classification tasks.
Users do not need to call it directly,
but just use `frbs.learn`

and `predict`

.

### Usage

1 2 3 | ```
FH.GBML(data.train, popu.size = 10, max.num.rule = 5, persen_cross = 0.6,
persen_mutant = 0.3, max.gen = 10, num.class, range.data.input,
p.dcare = 0.5, p.gccl = 0.5)
``` |

### Arguments

`data.train` |
a matrix ( |

`popu.size` |
the size of the population which is generated in each generation. |

`max.num.rule` |
the maximum number of rules. |

`persen_cross` |
a real number between 0 and 1 determining the probability of crossover. |

`persen_mutant` |
a real number between 0 and 1 determining the probability of mutation. |

`max.gen` |
the maximal number of generations for the genetic algorithms. |

`num.class` |
a number of the classes. |

`range.data.input` |
a matrix containing the ranges of the normalized input data. |

`p.dcare` |
a probability of "don't care" attributes occurred. |

`p.gccl` |
a probability of GCCL process occurred. |

### Details

This method is based on Ishibuchi's method using the hybridization of GCCL and the Pittsburgh approach for genetic fuzzy systems. The algorithm of this method is as follows:

Step 1: Generate population where each individual in the population is a fuzzy rule set.

Step 2: Calculate the fitness value of each rule set in the current population.

Step 3: Generate new rule sets by the selection, crossover, and mutation in the same manner as the Pittsburgh-style algorithm. Then, apply iterations of the GCCL to each of the generated rule sets with a probability.

Step 4: Add the best rule set in the current population to newly generated rule sets to form the next population.

Step 5: Return to Step 2 if the prespecified stopping condition is not satisfied.

### References

H. Ishibuchi, T. Yamamoto, and T. Nakashima, "Hybridization of fuzzy GBML approaches for pattern classification problems," IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 35, no. 2, pp. 359 - 365 (2005).