This paper presents a novel hybrid evolutionary algorithm for function optimization. In this algorithm, the space transformation search (STS) is embedded into a novel genetic algorithm (GA) which employs a novel crossover operator based on a nonconvex linear combination of multiple parents and elite-preservation strategy (EGT). STS transforms the search space to increase more opportunities for finding the global optimum and accelerate convergence speed. Experimental studies on 15 benchmark functions show that the STS-EGT not only has good ability to help EGT jump out of local optimum but also obtains faster convergence than the STS-GT which has no elitepreservation strategy.