An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images

Table 8

K-HAT value of each model varying with crossover ways.

Crossover way	Index
	K-HAT
	DBI	FCMI	PASI	DBFCMI	Average
P1	0.32	0.31	0.33	0.36	0.33
P2	0.36	0.34	0.24	0.48	0.34
P3	0.26	0.33	0.30	0.32	0.30
P4	0.29	0.35	0.34	0.36	0.34
P5	0.35	0.25	0.26	0.37	0.31
P6	0.23	0.20	0.10	0.39	0.23
Standard deviation	0.05	0.06	0.09	0.03	—

∗String length: 8, population: 30, selection way: roulette wheel selection, crossover rate: 0.8, and mutation rate: 0.003.