A New Replacement Strategy for Genetic Algorithm and Computational Experiments

In genetic algorithm, the replacement strategy defines how individual solutions are selected for survival into every new generation, and plays an important role in achieving balance between exploration and exploitation of the algorithm. In this paper, a new replacement strategy for genetic algorithm was proposed. Computational experiments on two combinatorial optimization problems, i.e, the multiple knapsack problem and the capacitated vehicle routing problem, were conducted. The results showed that the new replacement strategy improved the performance of the algorithm in terms of better solution obtained.