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deterministicoptimizationmethodsbasedontherealnaturalorganismsevolu-
tion.Theproblemoffindingasolutionofaminimizationproblemissimulated
on a population of different candidate propositions each representing proper-
tiesoftheproblem.Thesepropertiesaremodifiedineveryloopofasimulated
evolutionprocessandfinally,aftertheapplicationofdifferentgeneticoperators
imitatingthenaturalselection,thebestindividualisfound.Theoriginalsource
code, developed for this study has been written in the
Wolfram Mathematica
environment.ThecorrespondingblockdiagramisdepictedinFig6.
Fig. 6.
BlockdiagramoftheappliedGeneticAlgorithm
Inthefirststepaninitialpopulationmadefromrandomlygeneratedindivid-
ualsiscreated.Thesizeofpopulationcanbeselectedintheprogramconfigura-
tion.Thedurationofcalculationisobviouslyaffectedbythesizeofpopulation.
Individualsarecandidatesolutionsandtheyarerepresentedasasetofnum-
bers. Everynumber correspondsto one parameterof the mathematical model
of the problem. To obtain a physical solution we have to set the range of pa-
rametersvalues,andtosettheprecisionoftherepresentation,becausetheGAs
applythebinaryencodingofthevariables.
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