Abstract
This paper presents a genetic algorithm (GA) for scheduling problem of a robot control computation. That is very difficult problem which belongs to the class of NP-hard problems. The authors have already proposed several algorithms, which are based on heuristic and branch-and-bound approaches, for the scheduling problem. The conventional algorithms, however, have the limits of their ability in quality of solutions and computational time. The scheduling problem is a typical partitioning problem : partitioning objects into a fixed number of groups to optimize an objective function. Consequently, this paper proposes a new crossover method named weighted-edge crossover which preserves both the structure and the characteristic of the feasible solution of partitioning problem. Furthermore, in order to improve the performance of GA, this paper defines a distance between feasible solutions and uses it in the adaptive control of crossover rate. To demonstrate the effectiveness of the proposed GA, comparative study of the GA with the conventional algorithms is carried out on several computational experiments.
