PhD Student Helga Ingimundardóttir
PhD Student Helga Ingimundardóttir & Advisor Tómas Philip Rúnarsson
University of Iceland
Pinned Repositories
Beamer
Helga Ingimundardottir's PhD Beamer Presentations in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
Cheshire
Contains other repos as submodules
Code
Helga Ingimundardottir's PhD Source Code in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
Data
Helga Ingimundardottir's PhD Data in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
Paper-I
H. Ingimundardottir, T.P. Runarsson. Supervised learning linear priority dispatch rules for job-shop scheduling. In Proceedings of the 5th international conference on Learning and Intelligent Optimization (LION'05), Carlos Coello Coello (Ed.). Springer-Verlag, Berlin, Heidelberg, 263-277 (2011). doi:10.1007/978-3-642-25566-3_20
Paper-II
H. Ingimundardottir, T.P. Runarsson. Sampling Strategies in Ordinal Regression for Surrogate Assisted Evolutionary Optimization. In: 11th International Conference on. Intelligent Systems Design and Applications (ISDA), November 22-24, 2011. doi:10.1109/ISDA.2011.6121815.
Paper-III
H. Ingimundardottir, T.P. Runarsson. Determining the Characteristic of Difficult Job Shop Scheduling Instances for a Heuristic Solution Method. In: Learning and Intelligent OptimizatioN (LION6), January 16-20, 2012. doi:10.1007/978-3-642-34413-8_36.
Paper-IV
H. Ingimundardotir, T.P. Runarsson. Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling. In: 6th International Conference on Evolutionary Computation Theory and Applications (ECTA6), October 22-24, 2014. doi:10.5220/0005077200590067.
Paper-VI
Approved with minor revisions to Journal of Heuristics
Thesis
Helga Ingimundardottir's PhD Thesis in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
PhD Student Helga Ingimundardóttir's Repositories
ALICE-InRu/Thesis
Helga Ingimundardottir's PhD Thesis in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
ALICE-InRu/Paper-I
H. Ingimundardottir, T.P. Runarsson. Supervised learning linear priority dispatch rules for job-shop scheduling. In Proceedings of the 5th international conference on Learning and Intelligent Optimization (LION'05), Carlos Coello Coello (Ed.). Springer-Verlag, Berlin, Heidelberg, 263-277 (2011). doi:10.1007/978-3-642-25566-3_20
ALICE-InRu/Paper-VI
Approved with minor revisions to Journal of Heuristics
ALICE-InRu/Beamer
Helga Ingimundardottir's PhD Beamer Presentations in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
ALICE-InRu/Cheshire
Contains other repos as submodules
ALICE-InRu/Code
Helga Ingimundardottir's PhD Source Code in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
ALICE-InRu/Data
Helga Ingimundardottir's PhD Data in Computational Engineering : Adaptive Learning Intelligent Composite rulEs
ALICE-InRu/Paper-II
H. Ingimundardottir, T.P. Runarsson. Sampling Strategies in Ordinal Regression for Surrogate Assisted Evolutionary Optimization. In: 11th International Conference on. Intelligent Systems Design and Applications (ISDA), November 22-24, 2011. doi:10.1109/ISDA.2011.6121815.
ALICE-InRu/Paper-III
H. Ingimundardottir, T.P. Runarsson. Determining the Characteristic of Difficult Job Shop Scheduling Instances for a Heuristic Solution Method. In: Learning and Intelligent OptimizatioN (LION6), January 16-20, 2012. doi:10.1007/978-3-642-34413-8_36.
ALICE-InRu/Paper-IV
H. Ingimundardotir, T.P. Runarsson. Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling. In: 6th International Conference on Evolutionary Computation Theory and Applications (ECTA6), October 22-24, 2014. doi:10.5220/0005077200590067.
ALICE-InRu/Paper-V
H. Ingimundardotir, T.P. Runarsson. Generating Training Data for Learning Linear Composite Dispatching Rules for Scheduling. In: Learning and Intelligent OptimizatioN (LION9), January 12-16, 2015. Nominated for Best Paper Award.
ALICE-InRu/Papers
Contains all papers as submodules