Improvement of Real-valued Genetic Algorithm and Performance Study

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.