Performance Evaluation of a Parameter-Free Genetic Algorithm for Job-Shop Scheduling Problems
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چکیده
The job-shop scheduling problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1]. However, the tuning of genetic parameters has to be performed by trial and error, making optimization by GA ad hoc. To address this problem, Sawai et al. have proposed the Parameter-free Genetic Algorithm (PfGA), for which no control parameters for genetic operation need to be set in advance [3]. We have proposed an extended parameter-free GA for JSSP [2], and reported that the GA performed well without tedious parameter-tuning. This paper reports the result of empirical evaluation of the GA to a wider range of problem instances. The simulation results show that the GA performs well for many problem instances, and the performance can be improved greatly by increasing the number of subpopulations in the parallel distributed version.
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تاریخ انتشار 2003