Performance Measures on Jumping Gene - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 107

Table 5.8
Means.and.Standard.Deviations.of.the.Spread.for.Constrained.Test.Functions
Test Function MOGA NPGA2 NSGA2 SPEA2 PAES MICROGA JG
DEB 1.23439 1.2824 1.00997 1.10916 1.34785 1.01390 1.02604
(0.11148) (0.10698) (0.07196) (0.08286) (0.20127) (0.09970) (0.09947)
BEL 0.78913 0.78612 0.38570 0.48759 1.51643 0.64472 0.37570
(0.05983) (0.06286) (0.02923) (0.03960) (0.07172) (0.06951) (0.02594)
SRIN 0.93024 0.95896 0.39539 0.50727 1.37574 0.97593 0.38802
(0.08007) (0.08668) (0.03431) (0.03244) (0.15613) (0.09951) (0.03557)
TAN 1.03719 1.11753 1.13388 1.11497 0.79309 0.83511 1.13543
(0.11038) (0.10733) (0.09879) (0.11324) (0.24614) (0.10710) (0.12711)
BINH 1.04469 1.07929 0.55134 0.69212 1.26679 0.98301 0.55704
(0.12719) (0.12185) (0.11730) (0.09136) (0.18122) (0.17276) (0.14308)
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.