In this paper

a novel genetic algorithms with sexual reproduction is proposed to combat premature convergence inherent in Standard Genetic Algorithms(SGA) and speed up convergence. It imitates the sexual reproduction that is very popular in nature: (1) Each individual is encoded using diploid chromosomes which can save more information so as to memorize more good patterns

(2) There is a pair of sexual chromosome that reflects the sexual feature of each individual

so there are two kinds of individuals—male and female individuals

(3) During the reproduction procedure

each individual can only be matched with another individual with different sexual feature

and (4) Dominant genes decide the individual characters. Also

the corresponding crossover

mutation and selection operators for the sexual reproduction are developed in this paper. In the evolutionary procedure

the male individuals reserve higher mutation rate to obtain better global exploring ability while the female individuals have lower mutation rate to enhance local searching ability. As a result

the male individuals possess strong global exploring ability and the female individuals possess strong local searching ability. At the same time

the diploid encoding and dominance law diversify the gene pool. So the algorithm can help the evolutionary procedure to escape from possible local entrapment and obtain good tradeoff between exploration ability and exploitation ability. The experiments are taken on two types of optimization problems

(1) find maximum of minimum values of a series of classical and typical complex multi-modal functions

and (2) find the optimized rout for TSP problem. The experimental results have shown the good performance of genetic algorithms with sexual reproduction.