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...
General and craniofacial development are complex adaptive processes influenced by diversity
Hart, E., Brook, A. H., Brook-O'Donnell, M., Hone, A., Hughes, T., & Smith, R. (2014)
General and craniofacial development are complex adaptive processes influenced by diversity. Australian Dental Journal, 59(S1), 13-22. https://doi.org/10.1111/adj.12158
Complex systems are present in such diverse areas as social systems, economies, ecosystems and Biology and, therefore, are highly relevant to dental research, education and pr...
A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution.
Sim, K., Hart, E., & Paechter, B. (2012)
A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. In Parallel Problem Solving from Nature: PPSN XII, (348-357). https://doi.org/10.1007/978-3-642-32964-7_35
A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem i...
Cloud computing - the logistic fundament of future networked care
Thuemmler, C. (2010)
Cloud computing - the logistic fundament of future networked care. PublicTechnology.net,
Cloud computing has the potential to revolutionise data management in health care.
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...
A role for immunology in 'next generation' robots.
Hart, E., Ross, P., Webb, A., & Lawson, A. (2003)
A role for immunology in 'next generation' robots. In J. Timmis, P. Bentley, & E. Hart (Eds.), Artificial Immune Systems. ICARIS 2003, 46-56. https://doi.org/10.1007/978-3-540-45192-1_5
Much of current robot research is about learning tasks in which the task to be achieved is pre-specified, a suitable technology for the task is chosen and the learning process...
Controlling a simulated Khepera with an XCS classifier system with memory.
Webb, A., Hart, E., Ross, P. & Lawson, A. (2003)
Controlling a simulated Khepera with an XCS classifier system with memory. ISBN 9783540200574
Autonomous agents commonly suffer from perceptual aliasing in which differing situations are perceived as identical by the robots sensors, yet require different courses of act...
Requirements for getting a robot to grow up.
Ross, P., Hart, E., Lawson, A., Webb, A., Prem, E., Poelz, P. & Morgavi, G. (2003)
Requirements for getting a robot to grow up. ISBN 9783540200574