whaleOptimization: Whale Optimization Algorithm (Binary Whale Optimization...

In FSinR: Feature Selection

Description

Generates a search function based on the whale optimization algorithm. This function is called internally within the searchAlgorithm function. Binary Whale Optimization Algorithm \insertCiteKumar2018FSinR 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

whaleOptimization(population = 10, iter = 10, verbose = FALSE)

Arguments

population	The number of whales population
iter	The number of iterations of the algorithm
verbose	Print the partial results in each iteration

Value

Returns a search function that is used to guide the feature selection process.

Author(s)

Francisco Aragón Royón

References

\insertAllCited

Examples

## Not run:  

## The direct application of this function is an advanced use that consists of using this 
# function directly and performing a search process in a feature space
## Classification problem

# Generates the filter evaluation function
filter_evaluator <- filterEvaluator('determinationCoefficient')

# Generates the search function with WOA
woa_search <- whaleOptimization()
# Performs the search process directly (parameters: dataset, target variable and evaluator)
woa_search(iris, 'Species', filter_evaluator)

## End(Not run)

FSinR documentation built on Nov. 23, 2020, 5:10 p.m.