The generic genetic algorithm incorporates with rough set theory - An application of the web services composition

Wen Yau Liang, Chun Che Huang

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


Evolutionary computing (EC) techniques have been used traditionally used for solving challenging optimization problems. But the increase in data and information has reduced the performance capacity of the GA, but highlighted the cost of finding a solution by GA. In addition, the genetic algorithm employed in previous literature is modeled to solve one problem exactly. The GA needs to be redesigned, at a cost, for it to be applied to another problem. For these two reasons, this paper proposes a method for incorporating the GA and rough set theory. The superiority of the proposed GA in this paper lies in its ability to model problems and explore solutions generically. The advantages of the proposed solution approach include: (i) solving problems that can be decomposed into functional requirements, and (ii) improving the performance of the GA by reducing the domain range of the initial population and constrained crossover using rough set theory. The solution approach is exemplified by solving the problem of web services composition, where currently the general analysis and selection of services can be excessively complex and un-systemic. Based on our experimental results, this approach has shown great promise and operates effectively.

Original languageEnglish
Pages (from-to)5549-5556
Number of pages8
JournalExpert Systems with Applications
Issue number3 PART 1
Publication statusPublished - 2009 Apr

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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