Performance Measures on Jumping Gene - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 104

Table 5.5
Means.and.Standard.Deviations.of.the.Generational.Distance.for.Constrained.Test.Functions
Test
Function MOGA NPGA2 NSGA2 SPEA2 PAES MICROGA JG
DEB 0.00787 0.00676 0.01081 0.01014 0.01824 0.01842 0.01008
(0.00229) (0.00171) (0.00262) (0.00365) (0.02965) (0.00358) (0.00170)
BEL 0.01348 0.01301 0.01240 0.01445 0.00902 0.09445 0.01279
(0.00396) (0.00261) (0.00194) (0.00205) (0.00417) (0.02769) (0.00152)
SRIN 0.33370 0.34227 0.40167 0.41859 17.3122 1.82348 0.41630
(0.07496) (0.08955) (0.06420) (0.07971) (24.2239) (0.72811) (0.07284)
TAN 0.01352 0.00958 0.01189 0.01044 0.05525 0.02776 0.01149
(0.00385) (0.00237) (0.00630) (0.00335) (0.03728) (0.00744) (0.00521)
BINH 0.31397 0.30102 0.34141 0.28119 19.7117 1.31411 0.31083
(0.12715) (0.13577) (0.24490) (0.11206) (23.7084) (0.78961) (0.13814)
Source: . Data.from.Chan,.T..M.,.Man,.K..F.,.Kwong,.S.,.Tang,.K..S.,.A.jumping.gene.paradigm.for.evolutionary.
multiobjective.optimization,. IEEETransactionsonEvolutionaryComputation ,.12(2),.143-159,.2008.
Note: . The.best.result.for.each.test.function.is.marked.in.bold,.and.the.values.in.parentheses.represent.the.
standard.deviation.