## 2 Related Work  
Heuristic population seeding strategies offer the advantage of giving high-quality individuals at a preliminary phase and the ability to produce near-optimal solutions in a few generations; however, they have disadvantages regarding the diversity of individuals created, randomness, the opportunity to investigate more search space, and the ability to discover a global optimum [42; 43; 44; 45]. On the other hand, the random population seeding method provides benefits such as population variety, the effective investigation of the search space, and the discovery of the best solution. Random seeding has two drawbacks: it takes longer to determine the best solution and produces individuals that have the worst possible sequence. Due to the differences between these two kinds of population seeding techniques, we propose an effective population seeding methodology involving randomness, the diversity of individuals, and a probable sequence. One well-known alternative method for random population seeding in GAs, particularly for the TSP, is the nearest-neighbor (NN) tour creation heuristic. Using this strategy, individuals in the population seeding are built with the city that is nearest to the current city, and these good individuals may alter the later search in subsequent generations [23]. A greedy GA (GGA) was proposed by [PERSON] et al. [25] in which a gene bank (GB) is used for population seeding. By collecting the permutation of 'n' cities according to their distance, the GB is generated. By using the GGA approach, a population of individuals is produced from the GB that is above average in terms of fitness and has a short defining length, and using the TSP, the performance of the GGA is justified. [PERSON] et al. [26] proposed an enhanced GA with a sorted starting population based on the idea that better parents produce better children. This strategy generates a huge initial population pool, ranks it based on fitness values, and then excludes a subset of individuals with poor fitness. A new GA for the TSP was created by [PERSON] and [PERSON] [46]. The initial population was generated using a local search approach, and the GA was thoroughly evaluated using a large number of small-scale TSP cases. [PERSON] et al. [9] presented a comprehensive survey of several population seeding techniques used on the TSP. They attempted to evaluate the performance of population seeding strategies such as random, NN, gene bank, selective initialization, and sorted populations. The performance results of prior population seeding strategies were sorted by the error rate and convergence rate. The results showed that the NN population technique outperformed the other strategies tested. NN develops highly fit individuals, followed by selective initialization and gene bank approaches. Based on the quality of individuals generated, [PERSON] et al. [32] evaluated the effectiveness of several population seeding approaches, including random, gene bank, nearest-neighbor, selective initialization, and sorted population seeding procedures. Using TSP instances, experiments were conducted. The experimental results indicated the order of performance of various population seeding approaches in terms of the error rate and convergence rate. To successfully solve the TSP, a unique Vari-begin and Vari-diversity population seeding strategy was presented. The Individual Diversity factor, which is a key feature of the Vari-begin and Vari-diversity population seeding approach, refers to the variety in city permutation that each individual creates. As a result, the disadvantage of early convergence is mitigated [32]. For a permutation-coded GA, an effective population seeding method based on Ordered Distance Vectors (ODVs) was suggested using an elitist service transfer strategy. The TSP was selected as the testbed, and tests were carried out on benchmark TSP instances of various sizes.