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.
|頁（從 - 到）||214-219|
|期刊||Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics|
|出版狀態||Published - 2008 12月 1|
|事件||2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore|
持續時間: 2008 10月 12 → 2008 10月 15
All Science Journal Classification (ASJC) codes