Algorithm selection using deep learning without feature extraction
Alissa, M., Sim, K., & Hart, E. (2019)
Algorithm selection using deep learning without feature extraction. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. , (198-206). https://doi.org/10.1145/3321707.3321845
We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In ...
Simulating Dynamic Vehicle Routing Problems with Athos
Hoffman, B., Guckert, M., Chalmers, K., & Urquhart, N. (2019)
Simulating Dynamic Vehicle Routing Problems with Athos. In Proceedings of the 33rd International ECMS Conference on Modelling and Simulation ECMS 2019, (296-302). https://doi.org/10.7148/2019-0296
Complex routing problems, such as vehicle routing problems with additional constraints, are both hard to solve and hard to express in a form that is accessible to the human ex...
Finding feasible timetables using group-based operators.
Lewis, R. M. R. & Paechter, B. (2007)
Finding feasible timetables using group-based operators. IEEE Transactions on Evolutionary Computation. 11, 397-413. doi:10.1109/TEVC.2006.885162. ISSN 1089-778X
This paper describes the applicability of the so-called "grouping genetic algorithm" to a well-known version of the university course timetabling problem. We note that there a...