Publications

A Generic Scalable Method for Scheduling Distributed Energy Resources Using Parallelized Population-Based Metaheuristics.
Khalloof, H.; Jakob, W.; Shahoud, S.; Duepmeier, C.; Hagenmeyer, V.
2021. Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2. Ed.: K. Arai, 1–21, Springer Nature. doi:10.1007/978-3-030-63089-8_1

Incorporating Unsupervised Deep Learning into Meta Learning for Energy Time Series Forecasting.
Shahoud, S.; Khalloof, H.; Duepmeier, C.; Hagenmeyer, V.
2021. Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. Ed.: K. Arai, 326–345, Springer Nature. doi:10.1007/978-3-030-63128-4_25

A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics.
Khalloof, H.; Mohammad, M.; Shahoud, S.; Duepmeier, C.; Hagenmeyer, V.
2020. Proceedings of the 12th International Conference on Management of Digital EcoSystems (MEDES’20), 2nd - 4th November 2020, Online. Ed.: Richard Chbeir, 124–131, Association for Computing Machinery (ACM). doi:10.1145/3415958.3433041

A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics.
Khalloof, H.; Jakob, W.; Shadoud, S.; Duepmeier, C.; Hagenmeyer, V.
2020

A distributed modular scalable and generic framework for parallelizing population-based metaheuristics.
Khalloof, H.; Ostheimer, P.; Jakob, W.; Shahoud, S.; Duepmeier, C.; Hagenmeyer, V.
2020. Parallel Processing and Applied Mathematics : 13th International Conference, PPAM 2019, Bialystok, Poland, September 8–11, 2019, Revised Selected Papers, Part I. Ed: R. Wyrzykowski, 432–444, Springer. doi:10.1007/978-3-030-43229-4_37

Descriptive Statistics Time-based Meta Features (DSTMF): Constructing a better Set of Meta Features for Model Selection in Energy Time Series Forecasting.
Shahoud, S.; Khalloof, H.; Duepmeier, C.; Hagenmeyer, V.
2020. APPIS 2020: Proceedings of the 3rd International Conference on Applications of Intelligent Systems. Ed.: N. Petkov, 3378221, Association for Computing Machinery (ACM). doi:10.1145/3378184.3378221

Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments using Web and Microservice Technologies.
Shahoud, S.; Gunnarsdottir, S.; Khalloof, H.; Düpmeier, C.; Hagenmeyer, V.
2019. 11th International Conference on Management of Digital EcoSystems, MEDES. doi:10.1145/3297662.3365807

Superlinear Speedup of Parallel Population-Based Metaheuristics: A Microservices and Container Virtualization Approach.
Khalloof, H.; Ostheimer, P.; Jakob, W.; Shahoud, S.; Düpmeier, C.; Hagenmeyer, V.
2019. Intelligent Data Engineering and Automated Learning. Ed.: Hujun Yin, 386–393, Springer. doi:10.1007/978-3-030-33607-3_42

A distributed Modular Scalable and Generic Framework for Parallelizing Population Based Metaheuristics.
Khalloof, H.; Ostheimer, P.; Jakob, W.; Shahoud, S.; Duepmeier, C.; Hagenmeyer, V.
2019. 13th International Conference on Parallel Processing and Applied Mathematics (PPAM 2019), Bialystok, PL, September 8-11, 2019, Springer

A generic distributed microservices and container based framework for metaheuristic optimization.
Khalloof, H.; Jakob, W.; Liu, J.; Braun, E.; Shahoud, S.; Duepmeier, C.; Hagenmeyer, V.
2018. Proceedings of the Genetic and Evolutionary Conference Companion, Kyoto, J, July 15-19, 2018, 1363–1370, Association for Computing Machinery (ACM). doi:10.1145/3205651.3208253