All Outputs (6)

Real-world applications of evolutionary computing (2000)
Presentation / Conference Contribution
Cagnoni, S., Poli, R., Smith, G. D., Corne, D., Oates, M., Hart, E., …Fogarty, T. C. (2000). 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. The 37 revised papers presented were carefully reviewed and selected by the... Read More about Real-world applications of evolutionary computing.

Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. (2000)
Presentation / Conference Contribution
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 (128-1

This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction problems (CSPs). In some approaches, the penalties are set in advance and they do not change during a run. In other appro... Read More about Solving CSPs with evolutionary algorithms using self-adaptive constraint weights..

Stone Soup (2000)
Presentation / Conference Contribution
Paechter, B., Fogarty, T. C., Burke, E., Cumming, A., & Rankin, B. (2000). Stone Soup. In Practice and Theory of Automated Timetabling III

Post-publication timetabling. (2000)
Presentation / Conference Contribution
Cumming, A., Paechter, B., & Rankin, R. C. (2000). Post-publication timetabling. In 3rd International Conference on the Practice And Theory of Automated Timetabling, PATAT 2000 (107-108)

Solving CSPs using self-adaptive constraint weights: how to prevent EAs from cheating (2000)
Presentation / Conference Contribution
Eiben, A. E., Jansen, B., Michalewicz, Z., & Paechter, B. (2000). Solving CSPs using self-adaptive constraint weights: how to prevent EAs from cheating. In GECCO'00: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation (128-13

This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction problems (CSPs). In some approaches, the penalties are set in advance and they do not change during a run. In other approa... Read More about Solving CSPs using self-adaptive constraint weights: how to prevent EAs from cheating.

Optimising an evolutionary algorithm for scheduling (2000)
Presentation / Conference Contribution
Urquhart, N. B., Chisholm, K., & Paechter, B. (2000). Optimising an evolutionary algorithm for scheduling. In S. Cagnoni, R. Poli, G. D. Smith, D. Corne, M. Oates, E. Hart, …T. C. Fogarty (Eds.), Real-World Applications of Evolutionary Computing: EvoWor

This paper examines two techniques for setting the parameters of an evolutionary Algorithm (EA). The example EA used for test purposes undertakes a simple scheduling problem. An initial version of the EA was tested utilising a set of parameters that... Read More about Optimising an evolutionary algorithm for scheduling.