Genetic algorithms in forecasting of Internet shops demand
The general aim of this article is to present genetic algorithms as a tool, that can be used in de-mand forecasting in internet shops. First part of article identities factors, which have to be taken into consideration during analysing demand in internet shops, e.g. dispersion of demand, delivery time in-fluence and different e-marketing factors. Specific form of used demand function is shown in the next section of the article. Then genetic algorithm is defined by its genetic operators acting on bit strings (examples of the operators are: crossover, inversion, and mutation) and its method of credit allocation (fitness evaluation and selection). Next the method of identification of the function parameters using genetic algorithms is shown. The next part of article shows appliance of presented genetic algorithm. The advantages and disadvantages of proposed method are shortly discussed in summary.
|Date of creation:||2009|
|Date of revision:||2009|
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- Chodak, Grzegorz & Kwaśnicki, Witold, 2000. "Genetic algorithms in seasonal demand forecasting," MPRA Paper 34099, University Library of Munich, Germany.
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