Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Genetic Algorithms based on Maintaining a diversity of the population for Job-shop Scheduling Problem

다양성유지를 기반으로 한 Job-shop Scheduling Problem의 진화적 해법

  • Published : 2001.06.01
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Abstract

This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excellent in characteristic preservation and to maintain a diversity of population. We used Job-based order crossover(JOX). Since the schedules generated by JOX are not always active-schedule, we proposed a method to transform them into active schedulesby using the GT method with c)laracteristic preservation. We introduce strategies for maintaining a diversity of the population by eliminating same individuals in the population. Furthermore, we are not used mutation. Experiments have been done on two examples: Fisher s and Thompson s $lO\timeslO and 20\times5$ benchmark problem.

유전자알고리듬(Genetic Algorithm)은 확률적인 집단 탐색법이고 적응도함수의 형태에 관계없는 직접 탐색법이기 때문에 최근 최적화 방법으로 주목을 받고 있다. 본 논문에서는 Job-shop Schedule Problem에 대하여 교배방법으로 JOX를 사용하며, 효율적인 탐색을 위하여 탐색범위를 축소시키는 강제조작을 형질유전을 고려한 형질유전GT법을 제안하고, 세대교체에 있어 모집단의 다양성을 유지하기 위하여 집단 내에 동일한 개체를 배제하는 방법을 제안한다. 제안 알고리듬을 Fisher & Thompson의 FT10$\times$10 및 FT20$\times$5 문제에 적용하여 유효성을 실험적으로 검증한다.

Keywords

References

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