Incorporating Tabu Search into the Survivor Selection of Genetic Algorithm

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