Combining the advantages of a genetic algorithm and an artificial immune system,a novel genetic algorithm named immune genetic algorithm based on quasi secondary response(IGA-QSR) is proposed. IGA-QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy,automatic extinction,clonal propagation,diversity guarantee,and selection based on comprehensive fitness are also used in the process of IGA-QSR.Theoretical analysis,numerical examples of three benchmark mathematical optimization problems and a traveling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy (SGA-ES). Besides,IGA-QSR allows the designers to stop and restart the optimization process freely without losing the best results that have already been obtained.These properties make IGA-QSR be a feasible,effective and robust search algorithm for complex engineering problems.