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Project scheduling using a genetic algorithm with adaptable changing genetic operators

✍ Scribed by Tomoya Ikeuchi; Yoshitomo Ikkai; Dai Araki; Takenao Ohkawa; Norihisa Komoda

Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
252 KB
Volume
124
Category
Article
ISSN
0424-7760

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✦ Synopsis

Genetic algorithms (GA) have been widely used to solve planning problems. However, they require one to determine the optimal values of many genetic parameters, such as population sizes, crossover probability, mutation probability, and so on. To make matters worse, the most suitable combination of parameters for one problem is not always optimal for others. Therefore, these parameters should be tuned whenever the problem changes.

In this paper, we propose an adaptable GA mechanism that has autonomic parameter tuning for the composition of generic operators. This mechanism raises questions concerning the probability of genetic operators that acted effectively, that is, the probability that one operator created better individuals than the other operators. It also successively adjusts the combinations of genetic parameters suitable for the target problem. We applied the adaptable GA mechanism to a project scheduling model (PSM) and evaluated it with manual tuning methods.

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