Linkage-shredding genetic algorithm for reliability assessment of structural systems

The proposed algorithm combines the benefits and the efficiency of the linkage learning process of the gene expression messy genetic algorithm (GEMGA) to the ability of the shredding genetic operator to explore new significant search domains. By reducing the dimensionality of the problem through linkage learning, the number of generations needed to reach convergence is greatly reduced. To further ameliorate the GA search's efficiency, the Shredding operator is used to estimate the value of the reliability index in a given search direction by building and updating a fitness value matrix based on an evolutionary learning process. This will drastically reduce the number of structural analyses that are usually required during the reliability assessment of structural systems. Furthermore, an exploitation process is implemented during the search for the local optima to obtain accurate reliability indexes and quantify the contributions of various variables to the structural failure modes identified during the search process. Thus, the proposed algorithm provides detailed information about a structure's failure modes that would be helpful for optimizing the design and improving the structure's safety against local and global failures. Examples are provided to demonstrate the high efficiency and accuracy of the proposed hybrid genetic algorithm.