Abstract
To balance selection pressure and population diversity is a key issue of designing an effective genetic algorithm (GA). This paper proposes incorporating tabu search (TS) into GA to address the issue. Instead of running GA and TS by turns, the desirable strategy of TS is implanted in the survivor selection of GA as a filter for promising solutions. Consequently the selection pressure and population diversity of GA are controlled. Experimental results on six well known test functions show that the proposed approach can outperform GA significantly in terms of solution quality. The empirical analysis further validates the effects of the TS strategy. © 2008 IEEE.