Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Heuristic Combination Method for Solving Job-Shop Scheduling Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
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Although there has been a wealth of work reported in the literature on the application of genetic algorithms (GAs) to jobshop scheduling problems, much of it contains some gross over-generalisations, i.e that the observed performance of a GA on a small set of problems can be extrapolated to whole classes of other problems. In this work we present part of an ongoing investigation that aims to explore in depth the performance of one GA across a whole range of classes of jobshop scheduling problems, in order to try and characterise the strengths and weaknesses of the GA approach. To do this, we have designed a configurable problem generator which can generate problems of tunable dificulty, with a number of difierent features. We conclude that the GA tested is relatively robust over wide range of problems, in that it finds a reasonable solution to most of the problems most of the time, and is capable of finding the optimum solutions when run 3 or 4 times. This is promising for many real world scheduling applications, in which a reasonable solution that can be quickly produced is all that is required. The investigation also throws up some interesting trends in problem difficulty, worthy of further investigation.