Fast genetic algorithm based on pattern reduction

Shih Pang Tseng, Chun Wei Tsai, Ming Chao Chiang, Chu-Sing Yang

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


This paper presents a simple but efficient algorithm for enhancing the performance of GA or GA-based algorithms while retaining the diversity of the search directions. The proposed algorithm is motivated by the observation that some of the genes common to all the individuals during the evolution process can be considered as part of the final solutions and thus can be removed to eliminate the redundant computations at the later generations of the evolution process. To evaluate the performance of the proposed algorithm, we use it to solve the traveling salesman problem (TSP). The benchmarks for the TSP problem range in size from 574 up to 2,152 cities. For the three problems evaluated, our experimental results indicate that the proposed algorithm can reduce the computation time from 28% up to about 84% compared to that of traditional GA and GA-based algorithms alone.

Original languageEnglish
Article number4811277
Pages (from-to)214-219
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2008 Dec 1
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 2008 Oct 122008 Oct 15

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction


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