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

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號178621
期刊Scientific World Journal
2014
DOIs
出版狀態Published - 2014 一月 1

All Science Journal Classification (ASJC) codes

  • 生物化學、遺傳與分子生物學 (全部)
  • 環境科學 (全部)

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