Análise de competição familiar por algoritmos genéticos inspirados em modelos cinéticos de mercado

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dc.contributor.advisorOmar, Nizam
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2067336430076971por
dc.contributor.authorLuquini, Evandro
dc.creator.Latteshttp://lattes.cnpq.br/3096741164187005por
dc.date.accessioned2019-10-08T14:19:41Z
dc.date.accessioned2020-05-28T18:08:03Z
dc.date.available2020-05-28T18:08:03Z
dc.date.issued2019-08-27
dc.description.abstractFamily competition genetic algorithms are a variant of classical genetic algorithms that evolveapopulationbyonlyemphasizingthesubstitutionrules.Inthisversion,selectionfor reproductionfavoringthebestindividualsisreplacedbyastrategythatgivesequalchances to all members of the population. The individuals selected to remain in the population consist solely of those within the family, which is formed by the pair of individuals selected for reproduction and their descendants, without considering the general state of the simulation. Over time, several studies have been done to find new substitution rules to improve population diversity and the effectiveness of this approach. Our study aligns with this goal and introduces new substitution rules inspired by the kinetic market models of econophysics. These models have many similarities with family competition genetic algorithms, but, differing from them, kinetic market models were designed so that the simulation results in a stationary distribution. The dynamics of a kinetic market model resemble a minimization procedure because all the agents are progressively shifted to lower energy states, independent of the initial distribution. However, these models are capable of preventingcondensation:thesituationinwhichallagentsinthepopulationconvergetothe samestateorwhenoneagenthijacksallthesystemenergy.Thesepropertiesareusedinthis work to increase the diversity and effectiveness of family competition genetic algorithms. Through a statistical protocol, the new substitution rules were tested and analyzed against others in the literature. The results were positive for experiments with combinatorial problems. In addition, the introduction of evolutionary computing individuals into the kinetic market also produces results for econophysics. The new non-conservative kinetic market model that results from the proposed merging differs from its peers by displaying random walks for the sum of all agents’ money and simultaneously a scaling behavior for their distribution in the populationeng
dc.description.sponsorshipUniversidade Presbiteriana Mackenziepor
dc.formatapplication/pdf*
dc.identifier.citationLUQUINI, Evandro. Análise de competição familiar por algoritmos genéticos inspirados em modelos cinéticos de mercado. 2019. 86 f. Tese (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2019.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24297
dc.keywordsevolutionary algorithmseng
dc.keywordseconophysicseng
dc.keywordsexchange market modelseng
dc.keywordskinetic market modelseng
dc.keywordseconophysics models of inequalityeng
dc.keywordscomputational optimizationeng
dc.keywordsoptimization metaheuristicseng
dc.keywordsfamily competition genetic algorithmseng
dc.keywordspopulation diversityeng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectalgoritmos evolutivospor
dc.subjecteconofísicapor
dc.subjectmodelos de mercado de câmbiopor
dc.subjectmodelos cinéticos de mercadopor
dc.subjectmodelos de desigualdade econofísicospor
dc.subjectotimização computacionalpor
dc.subjectmeta-heurísticas de otimizaçãopor
dc.subjectalgoritmos genéticos de competição familiarpor
dc.subjectdiversidade populacionalpor
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRApor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/19871/EVANDO%20LUQUINI.pdf.jpg*
dc.titleAnálise de competição familiar por algoritmos genéticos inspirados em modelos cinéticos de mercadopor
dc.typeTesepor
local.contributor.board1Oliveira, Pedro Paulo Balbi De
local.contributor.board1Latteshttp://lattes.cnpq.br/9556738277476279por
local.contributor.board2Oliveira, Paulo Murilo Castro de
local.contributor.board2Latteshttp://lattes.cnpq.br/9735333846587797por
local.contributor.board3Schimit, Pedro Henrique Trigues
local.contributor.board4Basso, Leonardo Fernando Cruz
local.contributor.board4Latteshttp://lattes.cnpq.br/1866154361601651por
local.publisher.countryBrasilpor
local.publisher.departmentEscola de Engenharia Mackenzie (EE)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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