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Although many people have tried using genetic algorithms (GAs) for exam timetabling, far fewer have done systematic investigations to try to determine whether a GA is a good choice of method or not. We have extensively studied GAs that use one particular kind of direct encoding for exam timetabling. Perhaps not surprisingly, it emerges that this approach is not very good, but it is instructive to...
Much recent research has focussed on applying genetic algorithms (GAs) to real educational institution timetabling problems. This work is generally successful, but it is as yet unclear whether a simpler stochastic hillclimbing (SH) strategy would generally do just as well, and how both GA and SH might compare with the use of simulated annealing (SA) on timetabling problems. We begin to investigate...
We describe the General Examination/Lecture Timetabling Problem (GELTP), which covers a very broad range of real problems faced continually in educational institutions, and we describe how Evolutionary Algorithms (EAs) can be employed to effectively address arbitrary instances of the GELTP. Some benchmark GELTPs are described, including real and randomly generated problems. Results are presented for...
It is of major practical and theoretical interest to investigate the conditions under which a Genetic Algorithm (GA) will perform better or worse than Simulated Annealing (SA) and/or Stochastic Hill-climbing (SH). Confusion of terms makes this investigation difficult, but at least the following fairly precise question can be asked: “What landscapes exist on which the employment of recombination and...
Researchers are turning more and more to evolutionary algorithms (EAs) as a flexible and effective technique for addressing timetabling problems in their institutions. We present a class of specialised mutation operators for use in conjunction with the commonly employed penalty function based EA approach to timetabling which shows significant improvement in performance over a range of real and realistic...
In this paper, we generalise a previously-described model of the error-prone polymerase chain reaction (PCR) reaction to conditions of arbitrarily variable amplification efficiency and initial population size. Generalisation of the model to these conditions improves the correspondence to observed and expected behaviours of PCR, and restricts the extent to which the model may explore sequence space...
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