LkbGBMLMichigan {FKBL} | R Documentation |
This is the implementation of Michigan genetic method. This is a genetic algorithm where each individual represents a rule. With the given probability, the crossing operation is performed by swapping two partition labels between two individuals. With the given probability, the mutation is performed by with a 50% of probability changing to a random appropriate value each of the partitions labels. "label" means a reference to a division in the partition, coded as a positive integer. This two operations are done the number of iterations specified. This makes the population grow and diversify. The final result is a knowledge base made by gathering in a base the actual pupulation of rules. Described in chapter 5, pages 105-117 at Ishibuchi et al.\
LkbGBMLMichigan(P, gen=1000, cross=0.9, muta=0.01, train)
Takes the vector of partitions, the number of generations, the crossing probability, the mutation probability and the train data.
P |
The vector of partitions. |
gen |
The number of generations. |
cross |
The cross probability up to 1. |
muta |
The mutation probability up to 1. |
train |
The train dataset. |
Returns a knowledge base with the partitions and the rules.
begin{itemize}
data(P) data(trainA) LkbGBMLMichigan(P,1000,0.9,0.01,trainA)