Binary Whale Optimization Algorithm Kumar2018FSinR is an algorithm that simulates the social behavior of humpback whales. This algorithm employs a binary version of the bubble-net hunting strategy. The algorithm starts with an initial population of individuals, and in each iteration updates the individuals according to several possible actions: Encircling prey, Bubble-net attacking or Search for prey
Usage
woa(data, class, featureSetEval, population = 10, iter = 10, verbose = FALSE)
Arguments
data
A data frame with the features and the class of the examples
class
The name of the dependent variable
featureSetEval
The measure for evaluate features
population
The number of whales population
iter
The number of iterations of the algorithm
verbose
Print the partial results in each iteration
Value
A list is returned containing for each repetition of the algorithm:
bestFeatures
A vector with all features. Selected features are marked with 1, unselected features are marked with 0
bestFitness
Evaluation measure obtained with the feature selection
popIter
List that contains as many elements as iterations has the algorithm. Each of the elements in the list are matrices that represent the population in that iteration. In this matrix the individuals and the evaluation measure of each one are shown
# NOT RUN {## Whale Optimization Algorithm for iris dataset (filter method)woa(iris, 'Species', roughsetConsistency, population = 10, iter = 5, verbose = TRUE)
# }