TGA: A new integrated approach to evolutionary algorithms

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

Research output: Contribution to conferencePaperpeer-review

14 Citations (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.

Original languageEnglish
Number of pages8
Publication statusPublished - 2001
EventCongress on Evolutionary Computation 2001 - Seoul, Korea, Republic of
Duration: 2001 May 272001 May 30


OtherCongress on Evolutionary Computation 2001
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering


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