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Statements

Subject Item
n2:RIV%2F62690094%3A18450%2F12%3A50000214%21RIV13-MSM-18450___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Development of stock market is affected by many factors. It is difficult to predict changes in prices of stocks because of many parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables. Application of genetic algorithms can help find suitable pre-set of behavioral patterns, functions and its parameters. In this paper we describe creation and implementation genetic algorithms to existing multi-agent simulation. This existing simulation provides basic model of simulation of stock market members behavior. The main goal of this article is describe how to implement genetic algorithm into this type of simulation. The main advantage of using genetic algorithms is dynamically created decision process or function of each agent. Article describes process of creating decision, simulating behavior of agents which decision algorithm was created by genetic programming. Next point is to show, how can be this implementation of genetic algorithms used in learning process of simulation. Development of stock market is affected by many factors. It is difficult to predict changes in prices of stocks because of many parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables. Application of genetic algorithms can help find suitable pre-set of behavioral patterns, functions and its parameters. In this paper we describe creation and implementation genetic algorithms to existing multi-agent simulation. This existing simulation provides basic model of simulation of stock market members behavior. The main goal of this article is describe how to implement genetic algorithm into this type of simulation. The main advantage of using genetic algorithms is dynamically created decision process or function of each agent. Article describes process of creating decision, simulating behavior of agents which decision algorithm was created by genetic programming. Next point is to show, how can be this implementation of genetic algorithms used in learning process of simulation.
dcterms:title
Application of Genetic Algorithms in Stock Market Simulation Application of Genetic Algorithms in Stock Market Simulation
skos:prefLabel
Application of Genetic Algorithms in Stock Market Simulation Application of Genetic Algorithms in Stock Market Simulation
skos:notation
RIV/62690094:18450/12:50000214!RIV13-MSM-18450___
n13:predkladatel
n16:orjk%3A18450
n3:aktivita
n11:S
n3:aktivity
S
n3:cisloPeriodika
47
n3:dodaniDat
n9:2013
n3:domaciTvurceVysledku
n18:9382313 n18:2727749
n3:druhVysledku
n12:J
n3:duvernostUdaju
n6:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
123306
n3:idVysledku
RIV/62690094:18450/12:50000214
n3:jazykVysledku
n15:eng
n3:klicovaSlova
stock-market; multiagent simulation; genetic programming; evolution algorithms
n3:klicoveSlovo
n7:multiagent%20simulation n7:stock-market n7:evolution%20algorithms n7:genetic%20programming
n3:kodStatuVydavatele
NL - Nizozemsko
n3:kontrolniKodProRIV
[EE01EDF819EF]
n3:nazevZdroje
Procedia - social and behavioral sciences
n3:obor
n19:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n9:2012
n3:svazekPeriodika
2012
n3:tvurceVysledku
Štěpánek, Jiří Šťovíček, Jiří Cimler, Richard
s:issn
1877-0428
s:numberOfPages
5
n17:doi
10.1016/j.sbspro.2012.06.619
n14:organizacniJednotka
18450