research-article

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

Authors:

Alexandros Agapitos,

Jenya Kovalchuk,

Simon Mark LucasAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1163 - 1170

https://doi.org/10.1145/1389095.1389327

Published: 12 July 2008 Publication History

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.

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Cited By

Syu YKuo JFanjiang Y(2017)Time series forecasting for dynamic quality of web servicesJournal of Systems and Software10.1016/j.jss.2017.09.011134:C(279-303)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.jss.2017.09.011
Al-Hajj RAssi ABatch F(2016)An evolutionary computing approach for estimating global solar radiation2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)10.1109/ICRERA.2016.7884553(285-290)Online publication date: Nov-2016
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Fanjiang YSyu YKuo J(2016)Search based approach to forecasting QoS attributes of web services using genetic programmingInformation and Software Technology10.1016/j.infsof.2016.08.00980:C(158-174)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.infsof.2016.08.009
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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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July 12 - 16, 2008

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Cited By

Syu YKuo JFanjiang Y(2017)Time series forecasting for dynamic quality of web servicesJournal of Systems and Software10.1016/j.jss.2017.09.011134:C(279-303)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.jss.2017.09.011
Al-Hajj RAssi ABatch F(2016)An evolutionary computing approach for estimating global solar radiation2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)10.1109/ICRERA.2016.7884553(285-290)Online publication date: Nov-2016
https://doi.org/10.1109/ICRERA.2016.7884553
Fanjiang YSyu YKuo J(2016)Search based approach to forecasting QoS attributes of web services using genetic programmingInformation and Software Technology10.1016/j.infsof.2016.08.00980:C(158-174)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.infsof.2016.08.009
Syu YFanjiang YKuo JMa S(2015)Applying Genetic Programming for Time-Aware Dynamic QoS PredictionProceedings of the 2015 IEEE International Conference on Mobile Services10.1109/MobServ.2015.39(217-224)Online publication date: 27-Jun-2015
https://dl.acm.org/doi/10.1109/MobServ.2015.39
Kattan AFatima SArif M(2015)Time-series event-based predictionInformation Sciences: an International Journal10.1016/j.ins.2014.12.054301:C(99-123)Online publication date: 20-Apr-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.12.054
Konig RJohansson U(2014)Rule extraction using genetic programming for accurate sales forecasting2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)10.1109/CIDM.2014.7008669(210-216)Online publication date: Dec-2014
https://doi.org/10.1109/CIDM.2014.7008669
Stijven SVan den Bossche RVladislavleva EVanmechelen KBroeckhove JKotanchek M(2014)Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load ModelsGenetic Programming Theory and Practice XI10.1007/978-1-4939-0375-7_3(47-63)Online publication date: 10-Mar-2014
https://doi.org/10.1007/978-1-4939-0375-7_3
Agapitos AO'Neill MBrabazon A(2012)Evolving seasonal forecasting models with genetic programming in the context of pricing weather-derivativesProceedings of the 2012t European conference on Applications of Evolutionary Computation10.1007/978-3-642-29178-4_14(135-144)Online publication date: 11-Apr-2012
https://dl.acm.org/doi/10.1007/978-3-642-29178-4_14
Agapitos AO’Neill MBrabazon A(2012)Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather DerivativesFinancial Decision Making Using Computational Intelligence10.1007/978-1-4614-3773-4_6(159-188)Online publication date: 22-Jun-2012
https://doi.org/10.1007/978-1-4614-3773-4_6
Vassiliadis VDounias G(2011)Nature-Inspired Intelligence in Supply Chain ManagementSupply Chain Optimization, Design, and Management10.4018/978-1-61520-633-9.ch001(1-31)Online publication date: 2011
https://doi.org/10.4018/978-1-61520-633-9.ch001
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