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On the genetic programming of time-series predictors for supply chain management

Agapitos, A and Dyson, M and Kovalchuk, J and Lucas, SM (2008) On the genetic programming of time-series predictors for supply chain management. In: UNSPECIFIED, ? - ?.

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Abstract

Single and multi-step time-series predictors were evolved for forecasting minimum bidding prices in a simulated supply chain management scenario. Evolved programs were allowed to use primitives that facilitate the statistical analysis of historical data. An investigation of the relationships between the use of such primitives and the induction of both accurate and predictive solutions was made, with the statistics calculated based on three input data transformation methods: integral, differential, and rational. Results are presented showing which features work best for both single-step and multi-step predictions. Copyright 2008 ACM.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 03 Oct 2012 09:46
Last Modified: 17 Aug 2017 18:07
URI: http://repository.essex.ac.uk/id/eprint/4010

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