Stratified Opposition-Based Initialization for Variable-Length Chromosome Shortest Path Problem Evolutionary Algorithms ⋆ Aiman Ghannami 1,∗ , Jing Li 1 , Ammar Hawbani 1 , Ahmed Al-Dubai 2 Abstract Initialization is the first and a major step in the implementation of evolutionary algorithms (EAs). Although there are many common general methods to ini- tialize EAs such as the pseudo-random number generator (PRNG), there is no single method that can fit every problem. This study provides a new, flexible, diversity-aware, and easy-to-implement initialization method for a genetic algo- rithm for the shortest path problem. The proposed algorithm, called stratified opposition-based sampling (SOBS), considers phenotype and genotype diver- sity while striving to achieve the best fitness for the initialization population. SOBS does not depend on a specific type of sampling, because the main goal is to stratify the sampling space. SOBS aims at an initial population with higher fitness and diversity in the phenotype and genotype. To investigate the performance of SOBS, four network models were used to simulate real-world networks. Compared with the most frequently used initialization method, that is, PRNG, SOBS provides more accurate solutions, better running time with less memory usage, and an initial population with higher fitness. Statistical analysis showed that SOBS yields solutions with higher accuracy in 68%–100% of the time. Although this study was focused on the genetic algorithm, it can ⋆ This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (A Class) No. XDA19020102. * Corresponding author Email addresses: aiman@mail.ustc.edu.cn (Aiman Ghannami ), lj@ustc.edu.cn (Jing Li), anmande@ustc.edu.cn (Ammar Hawbani), a.al-dubai@napier.ac.uk (Ahmed Al-Dubai) 1 University of Science and Technology of China,JinZhai 96, Hefei,China. 2 University of Edinburgh,South Bridge, Edinburgh EH8 9YL, United Kingdom. Preprint submitted to Journal of L A T E X Templates November 14, 2020