Chromosome Representation - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 229

Table C.1
Total.Length.of.a.Chromosome.for.Each.
Unconstrained.and.Constrained.Test.Function.in.
the.Binary.Chromosome.Representation

TotalNumber
ofVariables

TotalLengthofa
Chromosome(bits) TestFunction

SCH 1 38

FON 3 69

POL 2 46

ZIT1,.ZIT2,.ZIT3 30 600

ZIT4 10 236

ZIT6 10 200

DEB 2 43

BEL 2 45

SRIN,.BINH 2 52

TAN 2 44

Source Data. from. Chan,. T.. M.,. Man,. K.. F.,. Kwong,.

S., Tang,.K..S.,.A.jumping.gene.paradigm.for.evo-
lutionary.multiobjective.optimization, IEEETrans-
actionsonEvolutionaryComputation, 12(2),.143-159,.
2008.

In. our. simulation,. every. variable. has. a. precision. of. six. decimal. places..
Therefore,.the.total.lengths.of.the.chromosomes.in.the.population.for.differ-
ent.test.functions.can.be.calculated;.they.are.listed.in. Table.C.1 .. The.details.
of.test.functions.can.found.in. Appendix.B .

References
. 1.. 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.

. 2.. Michalewicz,. Z.,. Genetic Algorithms + Data Structures = Evolution Programs ,.

Berlin:.Springer-Verlag,.1996.