Jumping Gene Computational Approach - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 47

START

Set chromosome i = 1

Yes

i > Total number of chromosomes

END

No

No

Random number [ i ][ j ] < Jumping rate?

i = j + 1 Set transposon j = 1

Yes

Yes No

Randomly select
cut and paste ( i, j )
or copy and paste ( i, j )

j > Total number of transposons

j = j + 1

Figure 3.14
Flowchart.of.transposition..(From.Chan,.T..M.,.Man,.K..F.,.Kwong,.S.,.Tang,.K..S.,.A.jumping.
gene.paradigm.for.evolutionary.multiobjective.optimization,. IEEETransactionsonEvolutionary
Computation ,.12(2),.143-159,.2008.)

Objective
value
evaluation

Yes

g = max
generation?

START

No

Set parameters
and generation
counter
g = 0

Selection Replacement

g ++

Jumping-gene
transposition

Initial
population

Output
the best
solution

Crossover

Objective
value
evaluation

Mutation

END

Figure 3.15
Genetic.cycle.of.the.JG.GA..(From.Tang,.K..S.,.Kwong,.S.,.Man,.K..F.,.A.jumping.genes.paradigm:.
Theory,.veriication.and.applications,. IEEECircuitsandSystemsMagazine, .8(4),.18-36,.2008.)