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

Table 6

K-HAT of each index varying with selection ways.

Selection way	Index
	K-HAT
	DBI	FCMI	PASI	DBFCMI	Average
Roulette wheel Selection	0.36	0.34	0.24	0.48	0.34
Rank selection	0.19	0.06	0.10	0.39	0.19
Standard deviation	0.12	0.20	0.10	0.02	—

∗String length: 8, population: 30, crossover rate: 0.8, crossover way: two-point crossover, mutation rate: 0.003.