About: Application of Genetic Algorithms in Stock Market Simulation     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
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. (en)
Title
  • Application of Genetic Algorithms in Stock Market Simulation
  • Application of Genetic Algorithms in Stock Market Simulation (en)
skos:prefLabel
  • Application of Genetic Algorithms in Stock Market Simulation
  • Application of Genetic Algorithms in Stock Market Simulation (en)
skos:notation
  • RIV/62690094:18450/12:50000214!RIV13-MSM-18450___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...iv/cisloPeriodika
  • 47
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 123306
http://linked.open...ai/riv/idVysledku
  • RIV/62690094:18450/12:50000214
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • stock-market; multiagent simulation; genetic programming; evolution algorithms (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • NL - Nizozemsko
http://linked.open...ontrolniKodProRIV
  • [EE01EDF819EF]
http://linked.open...i/riv/nazevZdroje
  • Procedia - social and behavioral sciences
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2012
http://linked.open...iv/tvurceVysledku
  • Cimler, Richard
  • Štěpánek, Jiří
  • Šťovíček, Jiří
issn
  • 1877-0428
number of pages
http://bibframe.org/vocab/doi
  • 10.1016/j.sbspro.2012.06.619
http://localhost/t...ganizacniJednotka
  • 18450
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 48 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software