On the harmonious mating strategy through tabu search

Chuan Kang Ting, Sheng Tun Li, Chungnan Lee

研究成果: Article同行評審

30 引文 斯高帕斯(Scopus)


Genetic algorithms (GAs) are well-known heuristic algorithms and have been applied to solve a variety of complicated problems. When adopting GA approaches, two important issues - selection pressure and population diversity - must be considered. This work presents a novel mating strategy, called tabu genetic algorithm (TGA), which harmonizes these two issues by integrating tabu search (TS) into GA's selection. TGA incorporates the tabu list to prevent inbreeding so that population diversity can be maintained, and further utilizes the aspiration criterion to supply moderate selection pressure. An accompanied self-adaptive mutation method is also proposed to overcome the difficulty of determining mutation rate, which is sensitive to computing performance. The classic traveling salesman problem is used as a benchmark to validate the effectiveness of the proposed algorithm. Experimental results indicate that TGA can achieve harmony between population diversity and selection pressure. Comparisons with GA, TS, and hybrids of GA and TS further confirm the superiority of TGA in terms of both solution quality and convergence speed.

頁(從 - 到)189-214
期刊Information sciences
出版狀態Published - 2003 十一月 15

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 理論電腦科學
  • 電腦科學應用
  • 資訊系統與管理
  • 人工智慧


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