[Retracted] An Information Entropy Embedding Feature Selection Based on Genetic Algorithm

Algorithm 3

Crossover and mutation in GA.

	Input: Population , CROSSOVER_RATE, MUTATION_RATE
	Output: New population
(1)	For father in do
(2)	= father
(3)	If generate 0-1 random numbers, less than CROSSOVER_RATE
(4)	Select another individual in the population and use that individual as the mother.
(5)	Randomly generate crossover points in the second half of the DNA sequence. gets the genes of the mother behind the junction.
(6)	If generate 0-1 random numbers, less than MUTATION_RATE
(7)	Randomly generate mutation points and change the value of this binary point
(8)	Put into list
	End for