TGA: A new integrated approach to evolutionary algorithms

C. K. Ting, S. T. Li, C. Lee

研究成果: Paper同行評審

14 引文 斯高帕斯(Scopus)


Genetic Algorithm (GA) is a well-known heuristic optimization algorithm. However, it suffers from the serious problem of premature convergence, which is caused mainly by the population diversity decreasing in evolution. In this paper, we propose a novel algorithm, called TGA, which integrates the memory structure and search strategy of Tabu Search (TS) with GA. As such, the selection efficiency is improved and the population diversity is maintained by incorporating the regeneration operator. The traveling salesman problem is used as a benchmark to evaluate TGA and compare it with GA and TS. Experimental results show that TGA gets the better performance than GA and TS in terms of both convergence speed and solution quality.

出版狀態Published - 2001
事件Congress on Evolutionary Computation 2001 - Seoul, Korea, Republic of
持續時間: 2001 5月 272001 5月 30


OtherCongress on Evolutionary Computation 2001
國家/地區Korea, Republic of

All Science Journal Classification (ASJC) codes

  • 一般電腦科學
  • 一般工程


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