GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

Table 3

Controlling parameters of metaheuristic algorithms.

Optimization algorithm	Parameter	Value
GGA-MLP, GA, ABC, WOA, MMPSO, MVO, GOA	Initial population size	30
	# iterations	200
GA, ABC, WOA, MMPSO, MVO, GOA	Initial population generation	Random population initialization (synaptic weights and biases are initialized randomly in the range [−2, 2])
GGA-MLP	Initial population generation	Greedy population initialization
GA, GGA-MLP	Probability of crossover	0.8
	Probability of mutation	0.05
	Elitism	30%
ABC	Random number (ɸ)	[−1, 1]
WOA	Vector	Linearly decreasing from 2 to 0
	Random vector	[0, 1]
	Constant (b)	1
	Random number (p)	[0, 1]
	Random number (l)	[−1, 1]
MMPSO	Acceleration coefficients	1.48
	Inertia weights ()	0.729
	Number of swarms	3
	Swarm size	10
MVO	Minimum wormhole existence probability	0.2
	Minimum wormhole existence probability	1
GOA		1
		0.00001
	L	1.5
	F	0.5