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Eiben, A. E., Jansen, B., Michalewicz, Z., & Paechter, B. (2000). Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. In D. Whitley (Ed.), GECCO-2000 : proceedings of the genetic and evolutionary computation conference, 128-134
This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction
problems (CSPs). In some approaches, the penalt...
Cagnoni, S., Poli, R., Smith, G. D., Corne, D., Oates, M., Hart, E., …Fogarty, T. C. (1999). Real-world applications of evolutionary computing. In Proceedings of EvoWorkshops 2000
This book constitutes the refereed proceedings of six workshops on evolutionary computation held concurrently as EvoWorkshops 2000 in Edinburgh, Scotland, UK, in April 2000.
Luchian, H., Paechter, B., Radulescu, V., & Luchian, S. (1999). Two evolutionary approaches to cross-clustering problems. In Proceedings of the 1999 Congress on Evolutionary Computation, 860-870. https://doi.org/10.1109/CEC.1999.782514
Cross-clustering asks for a Boolean matrix to
be brought to a quasi-canonical form. The problem has
many applications in image processing, circuit design,
Cree, N. D., Maher, M., & Paechter, B. (1999). The continuous equilibrium optimal network design problem: a genetic approach. In M. G. H. Bell (Ed.), Transportation Networks: Recent Methodological advances, 163-174
A genetic algorithm (GA) program for providing a solution to the Continuous Equilibrium Network Design Problem (NDP) is introduced following a general discussion of the networ...
Paechter, B., Rankin, B., & Cumming, A. (1998). Improving a lecture timetabling system for university wide use. In Practice and Theory of Automated Timetabling II, 156-165. https://doi.org/10.1007/BFb0055887
During the academic year 1996/97 the authors were commissioned by their institution to produce an automated timetabling system for use by all departments within the Faculty of...
Paechter, B., Luchian, H., Cumming, A., & Petriuc, M. (1994). Two solutions to the general timetable problem using evolutionary algorithms. In Proceedings of the IEEE World Congress in Computational Intelligence, 300-305. https://doi.org/10.1109/ICEC.1994.349935
The general timetable problem, which involves the placing
of events requiring limited resources into timeslots,
has been approached in many different ways. This paper
Paechter, B., Fogarty, T. C., Burke, E., Cumming, A. & Rankin, B. (1999). Stone Soup. In Practice and Theory of Automated Timetabling IIIISBN 3-540-42421-0
Anderson, T., Paechter, B. & Lea, A. (1993). Investigating the acute physiology and chronic health evaluation II (APACHE II) data set for additional predictive power using neural networks. In Proceedings of the International Conference on Neural Networks & Expert Systems in Medicine & Healthcare
Lewis, R. M. R. & Paechter, B. (2003). New crossover operators for timetabling with evolutionary algorithms. In Lotfi, A. (Ed.). 5th International Conference on Recent Advances in Soft Computing, 189-195. ISBN 1-84233-110-8
When using an evolutionary algorithm (EA) to optimise a population of feasible course timetables, it is important that the mutation and crossover operators are designed in suc...
Cumming, A. & Paechter, B. (2000). Post-publication timetabling. In 3rd International Conference on the Practice And Theory of Automated Timetabling, PATAT 2000