A new evolutionary learning algorithm (HIGAPSO) based on a hybrid of real-code genetic algorithm (GA) and particle swarm optimization (PSO) is proposed in this paper.In this hybrid algorithm some improved genetic mechanisms

for example initial population produced by chaos sequence

non-linear ranking selection

competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted;also the new population is produced through three approaches

i.e.elitist strategy

PSO strategy and the improved genetic algorithm (IGA) strategy.Through testing four benchmark functions with large dimensionality

the experimental results show that this new algorithm not only improves the global optimization performance and quickens the convergence speed

but also obtains robust results with good quality

which indicates it is a promising approach for solving global optimization problems.