A high-performance genetic algorithm: Using traveling salesman problem as a case

Chun Wei Tsai, Shih Pang Tseng, Ming Chao Chiang, Chu Sing Yang, Tzung Pei Hong

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

Original languageEnglish
Article number178621
JournalScientific World Journal
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)


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