A similarity based neighborhood exploring strategy is proposed to deal with the mechanism of generating new individuals randomly in evolutionary computation.The new algorithm is called Similarity Based Evolutionary Algorithm（SBEA）.Neighborhood exploring brings the ability to self-adaptively generate new individuals easily.Aiming at balancing search results and search speed

we adopt the search strategy to classify the individuals by their fitness.Individuals’ classification differentiate respective function in search process

that is the excellent individuals mine the local optimal solution and others explore the search domain to find new local optimal solution.The experiment results indicate the new algorithm is very efficient for the optimization of low-dimension multi-modal functions.We can obtain all the global optimal solutions easily and quickly.As to high-dimension multi-modal functions

the results are also satisfactory.