Proceedings Intelligent Information Systems. IIS'97
DOI: 10.1109/iis.1997.645212
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Genetic forecasting algorithm with financial applications

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Cited by 5 publications
(2 citation statements)
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“…As a genetic algorithm evolves a set of solutions (Yu 2001), it has a higher probability of obtaining the most optimized solution. Also, the GAs search process can avoid the differentiation, escapes from the local minimum, and takes into account every related component (Chiraphadhanakul, Dangprasert, and Auatchanakorn 1997). Due to the abovementioned advantages, the agents on the proposed system use the genetic algorithmic approach to solve the modeled knapsack problem as discussed in Section 3.2.2.…”
Section: Localized Processing By Distributed Agentsmentioning
confidence: 99%
“…As a genetic algorithm evolves a set of solutions (Yu 2001), it has a higher probability of obtaining the most optimized solution. Also, the GAs search process can avoid the differentiation, escapes from the local minimum, and takes into account every related component (Chiraphadhanakul, Dangprasert, and Auatchanakorn 1997). Due to the abovementioned advantages, the agents on the proposed system use the genetic algorithmic approach to solve the modeled knapsack problem as discussed in Section 3.2.2.…”
Section: Localized Processing By Distributed Agentsmentioning
confidence: 99%
“…The statistical approach consists in moving average, exponential smoothing, time series, regression, and economic modelling [3]. The statistical approach consists in moving average, exponential smoothing, time series, regression, and economic modelling [3].…”
Section: Introductionmentioning
confidence: 99%