## an investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem (2002)

Venue: | Proceedings of the Congress on Evolutionary Computation 2002, CEC 2002 |

Citations: | 32 - 12 self |

### BibTeX

@INPROCEEDINGS{Cowling02aninvestigation,

author = {Peter Cowling and Graham Kendall and Limin Han},

title = {an investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem},

booktitle = {Proceedings of the Congress on Evolutionary Computation 2002, CEC 2002},

year = {2002},

pages = {1185--1190}

}

### Years of Citing Articles

### OpenURL

### Abstract

Abstract-This paper investigates a genetic algorithm based hyperheuristic (hyper-GA) for scheduling geographically distributed training staff and courses. The aim of the hyper-GA is to evolve a good-quality heuristic for each given instance of the problem and use this to find a solution by applying a suitable ordering from a set of low-level heuristics. Since the user only supplies a number of low-level problem-specific heuristics and an evaluation function, the hyperheuristic can easily be reimplemented for a different type of problem, and we would expect it to be robust across a wide range of problem instances. We show that the problem can be solved successfully by a hyper-GA, presenting results for four versions of the hyper-GA as well as a range of simpler heuristics and applying them to five test data set 1.

### Citations

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(Show Context)
Citation Context ...to apply them We use one-point crossover and a mutation operator which randomly selects some positions in one chromosome and mutates integers at these positions to other values ranging from 0 to 11, (=-=Davis, 1991-=-). After empirical testing over a range of parameter rates, we use 0.6 for crossover rate, 0.1 for mutation rate, a population size of 30, 200 generations (100 generations gives equally good results, ... |

525 |
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(Show Context)
Citation Context ...ent runs of the PPPN hyper-GA on a relatively difficult problem instance (the Basic data set), to each other data set. An upper bound (in table 3) is calculated by solving a relaxed knapsack problem (=-=Martello and Toth, 1990-=-) where we ignore travel penalties. Finally, in order to see the efficiency and the robustness of the hyper-GA, we compare our genetic and memetic algorithms, each low-level heuristic considered alone... |

86 | A hyperheuristic approach for scheduling a sales summit - Cowling, Kendall, et al. - 2000 |

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66 |
Nurse scheduling with tabu search and strategic oscillation
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(Show Context)
Citation Context ...nvolve the allocation of staff to timeslots and possibly locations (Wren, 1995a). Personnel scheduling covers many areas, such as the nurse rostering problem (Burke et al, 1998; Burke et al, 2001 and =-=Dowsland, 1998-=-), transportation staff scheduling (Wren, 1995b), educational institute staff scheduling (Schaerf, 1999) and airline crew scheduling (Emden-Weinert and Proksch, 1999). Metaheuristic approaches have be... |

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- 1999
(Show Context)
Citation Context ...overs many areas, such as the nurse rostering problem (Burke et al, 1998; Burke et al, 2001 and Dowsland, 1998), transportation staff scheduling (Wren, 1995b), educational institute staff scheduling (=-=Schaerf, 1999-=-) and airline crew scheduling (Emden-Weinert and Proksch, 1999). Metaheuristic approaches have been applied successfully to a range of personnel scheduling problems. For example, Aickelin and Dowsland... |

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(Show Context)
Citation Context ...r a period of adaptation, and applied it to a network scheduling problem. Indirect genetic algorithms have been studied by a number of researchers, such as Terashima-Marin, Ross and ValenzuelaRendon (=-=Terashima-Marin et al, 1999-=-), who designed an indirect GA to solve an examination timetabling problem. They have three strategies for the timetabling problem. Theirsrepresentation is a 10-position array which encodes the three ... |

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(Show Context)
Citation Context ...euristics and the amount of time elapsed since each heuristic was last called. By using the choice function, they can select which low-level heuristic to call next effectively. Hart, Ross and Nelson (=-=Hart et al, 1998-=-) develop an evolving heuristically driven schedule builder for a real-life chicken catching and transportation problem. In the application they divide the problem into two sub-problems and solve each... |

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3 |
On Evolution, Search, Optimisation, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms, report 826
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(Show Context)
Citation Context ...84 2896/1791 0/1/ 100 194/1729 1448/1750 2071/1779 TABLE 1. COMPARISON OF PARAMETERS FOR GA C: CROSSOVER, M: MUTATION P: POPULATION, G: GENERATION TIME/OBJECTIVE We also designed a memetic algorithm (=-=Moscato, 1989-=-) based on the direct genetic algorithm introduced above. Each member of the population is improved using one of a set of low-level heuristics that we will describe in section 3.2.1. The local search ... |