Evolutionary Algorithms (Ea) as Optimization Tools

7/21/2019 Evolutionary Algorithms (Ea) as Optimization Tools

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EVOLUTIONARY ALGORITHMS (EA) AS OPTIMIZATION

TOOLS

(CLASS SEMINAR)CVE 697

BY

AYENIGBA, SOGO EMMANUEL

08/30GB096

AN

IGE, E!AR OLU!ASEUN0"/30GB0#7

EPARTMENT O$ CIVIL ENGINEERING

UNIVERSITY O$ ILORIN, ILORIN



ABSTRACT

• The need for nding optimal solutions in problemscomes with the extremities of designing formaximum cost or minimum reliability. Henceoptimi!ation methods are of great importance."#er the years se#eral research and applications

in optimi!ation has been seen. This study focuseson one of the non$traditional optimi!ationmethods which is %#olutionary Algorithms &%As'.%As o(er practical ad#antages to researchers

facing di)cult optimi!ation problems due to itsrobust response to changing circumstances*exibility among other facets. The study alsohighlights the principle behind %As which is the+arwin,s principle of %#olution. The general

characteristics which include selection and



/TR"+0CT"/

• "ptimi!ation – +enition

– 1athematical and %ngineering"ptimi!ation

– History

– /on$traditional method of solution



+%2/T"/ "2 %As

• %#olutionary algorithms &%As' arepopulation$based metaheuristicoptimi!ation algorithms that use

biology$inspired mechanisms li3emutation crosso#er naturalselection and sur#i#al of the ttest

in order to rene a set of solutioncandidates iterati#ely



BASC 4R/C45% 2R"1/AT0R%

• +arwin,s principle of naturalselection



-%/%RA5 CHARACT%RSTCS "2 %As

• Set of solution candidates

• Selection process

•

Recombination and mutation



-%/%RA5 SCH%1% "2 %A



-%/%RA5 SCH%1% "2 %Acontd6



T74%S "2 %As

• -enetic algorithms

• %#olutionary strategy

•

%#olutionary programming• -enetic programming



C"/C50S"/• "ptimi!ation methods are of great importance. The

need for nding optimal solutions in problems comes

with the extremities of designing for maximum cost orminimum reliability.

• "#er the years se#eral research and applications inoptimi!ation has been seen. This study showed that

there were se#eral methods that had been used intrying to sol#e optimi!ation problems includingtraditional &mathematical' and non$traditional methods.

• The study focused on non$traditional optimi!ationmethods which is %#olutionary Algorithms &%As'. %As

o(er practical ad#antages to researchers facing di)cultoptimi!ation problems due to its robust response tochanging circumstances *exibility among other facets.



C"/C50S"/ contd6.

• %As was dened as a population$based

metaheuristic optimi!ation algorithms that usebiology$inspired mechanisms li3e mutationcrosso#er natural selection and sur#i#al of thettest in order to rene a set of solution

candidates iterati#ely The study highlighted theprinciple behind %As which is the +arwin,sprinciple of %#olution. The general characteristicswhich include selection and recombination were

also discussed. Among the se#eral types of %As-enetic algorithm %#olutionary strategy -eneticprogramming and %#olutionary programmingwere discussed.



R%C"11%/+AT"/

• 2urther study to be carried out withthe aim of de#eloping %#olutionaryAlgorithms procedure.

• %As should be gi#en more classroomhours so as to enable the studentsbenet more from its rich resource.



THA/8 7"02"R 5ST%//-

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Evolutionary Algorithms (Ea) as Optimization Tools