Performance Measures on Jumping Gene - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 101

Table 5.3
Parameters.of.MOEAs

Parameter Value/Type

Chromosome.encoding.method Binary.and.real.representation

Population.size.(MOGA,.NPGA2,.
NSGA2,.SPEA2,.JG)

100

Population.size.(PAES) 1

Population.size.(MICROGA) 4

Maximum.generations.(MOGA,.
NPGA2,.NSGA2,.SPEA2,.JG)

SCH,.FON,.POL,.DEB,.BEL,.SRIN,.TAN,.BINH:.20
ZIT1 a ,.ZIT2 a ,.ZIT3 a :.80
ZIT4 a :.200
ZIT6 a :.150

Maximum.iterations.(PAES) SCH,.FON,.POL,.DEB,.BEL,.SRIN,.TAN,.BINH:.2,000

ZIT1 a ,.ZIT2 a ,.ZIT3 a :.8,000
ZIT4 a :.20,000
ZIT6 a :.15,000

Maximum.iterations.(MICROGA) SCH,.FON,.POL,.DEB,.BEL,.SRIN,.TAN,.BINH:.500

ZIT1 a ,.ZIT2 a ,.ZIT3 a :.2,000
ZIT4 a :.5,000
ZIT6 a :.3,750

Crossover.type Uniform.crossover

Crossover.rate 0.8

Mutation.rate 1/ N .or.1/ L .where. N .is.the.total.number.of.variables.

for.real-coded.MOEAs.and. L .is.the.total.number.of.
bits.in.a.chromosome.for.binary-coded.MOEAs

Jumping.rate.(JG) 0.04

Number.of.transposons.(JG) 1

Length.of.transposons.(JG) 3

Archive.size.(SPEA2,.PAES) 100

Depth.(PAES) 4

Size.of.external.memory.(MICROGA) 100

Size.of.population.memory.
(MICROGA)

80

Percentage.of.non-replaceable.memory.
(MICROGA)

0.25

Replacement.cycle.(MICROGA) Every.25.iterations

Number.of.subdivisions.of.the.
adaptive.grid.(MICROGA)

25

Number.of.iterations.to.achieve.
nominal.convergence.(MICROGA)

4

Source: . Data.from.Chan,.T..M.,.Man,.K..F.,.Kwong,.S.,.Tang,.K..S.,.A.jumping.gene.paradigm.
for evolutionary. multiobjective. optimization,. IEEE Transactions on Evolutionary
Computation , 12(2),.143-159,.2008.
Note: . NPGA2,. niched. Pareto. genetic. algorithm. 2;. PAES,. Pareto. archived. evolution. strategy;.
SPEA2,.strength.Pareto.evolutionary.algorithm.2.
a. Same.maximum.generations.(iterations).for.binary-coded.and.real-coded.MOEAs.