Introduction - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 4

1.2 OrganizationofChapters
This. topic. has. nine. chapters,. including. the. introduction. in. this. chapter.. In.
Chapter. 2 ,. a. thorough. review. of. state-of-the-art. multiobjective. optimization.
techniques.is.presented..This.leads.the.way.for.new.development.of.evolutionary.
theory.such.as.JG.to.further.unravel.the.shortfall.of.multiobjective.optimization,.
particularly.when.convergence.and.diversity.are.both.in.demand.
Considering. the. fact. that. the. biogenetic. phenomenon. of. the. JG. may. be.
new.to.readers,.the.basic.biological.gene.transposition.process.is.described.
in. Chapter.3 ..It.serves.as.a.shortcut.to.the.bioscience.for.comprehension.of.
the. JG. concept.. The. transformation. of. gene. manipulation. in. the. computa-
tional.forms,.namely,.copy.and.paste.and.cut.and.paste,.is.then.presented,.
supported.by.a.series.of.studies.on.its.suitability.
As. the. JG. is. such. a. new. algorithm,. it. is. preferable. to. derive. the. neces-
sary. proofs. of. the. JG. mathematically. at. the. beginning.. A. number. of. theo-
retical. proposals. in. this. endeavor. have. been. under. consideration,. but. it.
was.the.schema.theory.deployed.by.Stephens.and.Waelbroeck's.model.that.
became.the.winner.. Chapter.4 .provides.an.exact.mathematical.formulation.
for.the.growth.of.the.schemata;.consequently,.two.delightful.mathematical.
equations,. one. for. the. copy-and-paste. and. the. other. for. the. cut-and-paste.
operations.are.established.and.presented..Further.efforts.to.derive.important.
theories. for. understanding. the. dynamics. of. the. growth. of. schemata,. and.
methods.have.been.established.for.verifying.all.the.analytical.findings..All.
these.results.are.given.in. Chapter.4 .
Knowing.that.solutions.for.an.MOP.can.be.measured.by.their.convergence.
and.diversity,.a.number.of.suitable.metrics.are.explained.in. Chapter.5 ..All.
these.were.statistically.conducted.based.on.a.range.of.benchmark.mathemat-
ical. functions,. whether. constrained. or. unconstrained.. The. JG. was. singled.
out.in.most.of.the.tests,.as.clearly.illustrated.in. Chapter.5 .
This.successful.work.in.JG.development.resulted.in.high.confidence,.natu-
rally.leading.to.the.area.of.practical.applications.. Chapter.6 .addresses.the.irst.
application,.in.which.the.radio-to-fiber.repeater.placement.in.wireless.local.
loop.systems.was.studied..In. Chapter.7 ,.another.application,.a.resource.man-
agement.problem.in.the.wideband.code.division.multiple.access.(WCDMA).
system,.is.given..The.final.application.discussed.is.base.station.placement.in.
wireless.local-area.networks,.with.details.given.in. Chapter.8 ..The.problem.is.
tackled.of.placing.base.stations.in.appropriate.positions.to.provide.sufficient.
radio.coverage.quality.for.each.terminal..It.is.a.typical.minimax.optimiza-
tion.problem.for.which.adequate.solutions.may.not.be.easily.obtained.
All.these.examples.demonstrated.that.the.JG.can.indeed.be.a.new.but.use-
ful.addition.to.the.existing.evolutionary.algorithms.when.a.better.result.in.
trading. off. the. measure. between. convergence. and. diversity. performance.
metrics.is.desired..Finally,.a.short.summary.is.given.in. Chapter.9 .regarding.
all.the.work.done.to.date.on.this.topic.