Dynamic real-time scheduling for multi-processor tasks using genetic algorithm

Shu Chen Cheng, Yueh Min Huang

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)

Abstract

With the exponential growth of time to obtain an optimal solution, the job-shop scheduling problems have been categorized as NP-complete problems. The time complexity makes the exhaustive search for a global optimal schedule infeasible or even impossible. Recently, genetic algorithms have shown the feasibility to solve the job-shop scheduling problems. However, a pure GA-based approach tends to generate illegal schedules due to the crossover and the mutation operators. It is often the case that the gene expression or the genetic operators need to be specially tailored to fit the problem domain or some other schemes may be combined to solve the scheduling problems. This paper presents a GA-based approach with a feasible energy function to generate good-quality schedules. This work concentrates mainly on dynamic real-time scheduling problems with constraint satisfaction. In our work, we design an easy-understood genotype to generate legal schedules and induce that the proposed approach can converge rapidly to address its applicability.

Original languageEnglish
Pages (from-to)154-160
Number of pages7
JournalProceedings - International Computer Software and Applications Conference
Volume1
Publication statusPublished - 2004 Dec 1
EventProceedings of the 28th Annual International Computer Software and Applications Conference, COMPSAC 2004 - Hong Kong, China, Hong Kong
Duration: 2004 Sep 282004 Sep 30

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications

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