In order to reduce scale of test cases and to reduce the cost of regression test

combine genetic algorithm with greedy algorithm

propose a hybrid genetic algorithm to solve the problem of test case minimization.Selection

crossover and mutation operation in standard genetic algorithm are improved by this algorithm

which effectively improve global optimizing ability of the algorithm. Meanwhile

greedy algorithm is used to deal with feasible solutions and infeasible solutions

which can improve local optimizing ability of genetic algorithm.Experimental results show that compared with the standard genetic algorithm

the hybrid algorithm can obtain better reduction effect and faster convergence speed under the premise of guaranteeing the completeness of the test.