Improved Estimation of Distribution Algorithms Based on Gaussian Distribution
Shang Gao, Cungen Cao · Research Journal of Applied Sciences Engineering and Technology · 2013
Estimation of Distribution Algorithms (EDAs) is a new kind of evolution algorithm. In EDAs, through the statistics of the information of selected individuals in current group, the probability of the individual distribution in next generation is given and the next generation of group is formed by random sampling. An improved estimation of distribution algorithms based on normal distribution is presented for function optimization in continuous space. The algorithm regarded the selected individual as a normal distribution and the random new populations of normal distribution were generated and some selection of individual are crossed with the best solution. Compared with estimation of distribution algorithms based on uniform distribution and estimation distribution algorithm based on normal distribution, the improved estimation distribution algorithm based on normal distribution is more effective through result. At last, better population selection proportions are analyzed.