A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification

Algorithm 4

Pseudocode of CBCSEM based on feature selection.

	Input: labelled data, max_iterations or stop criteria, number of nests(m), number of dimensions(features) d, CS parameters, Lb, Ub, classifer models.
	Output: Best fitness, best features.
(1)	n_nests = 50, n_features = dimensions(d), nest_fitness[] = −10,
(2)	Initialize two populations performs the following operations:
(3)	for each nest in population do
(4)	for each dimension in one nest do
(5)	Randomly assign values to two populations of individuals based on [Lb, Ub]. Updating populations based on chaotic maps equations (7) and (8)
(6)	end
(7)	Convert two populations to binary using equations (3) and (4)
(8)	Calculate the fitness for individuals of two populations by equation (12)
(9)	Update the nest_fitness
(10)	end
(11)	Both populations perform Algorithm 3
(12)	while tmax_iterations or stop criteria do
(13)	for each nest in populations do
(14)	perform levy flights to generate new populations use equation (9) and (10)
(15)	end
(16)	for each nest in populations do
(17)	Rectification procedure
(18)	Normalize the population value generated by Lévy flight
(19)	Binary conversion
(20)	end
(21)	Then execute Algorithm 2
(22)	end
(23)	if nest_fitness < mut_fitness then
(24)	repalce the nest with mut_pop(i)
(25)	end
(26)	return mut_fitness