## 2 Methodology
### SEGA Algorithm  
The SEGA algorithm is a genetic algorithm that uses the elite retention strategy [36]. This genetic algorithm is similar to the biological evolutionary process, where only the fittest chromosomes are retained [37]. In a genetic algorithm, the competition between individual chromosomes is achieved by calculating the fitness of each chromosome. Chromosomes with high fitness have a higher probability of surviving and passing their genes on to the next generation, while those with low fitness have a higher probability of being eliminated. Several natural selection strategies can be used, including roulette and tournaments [38]. The tournament method has the advantage of not easily falling into local optimal solutions, is not very complex, and allows for parallelization processing. It is therefore the most popular selection strategy in genetic algorithms. Algorithm 1 shows the basic process of the SEGA algorithm. The algorithm starts by selecting a random part of the solutions from the solution space of the problem as the initial population. It considers each solution in the population as a chromosome, which is a sequence of numbers or a sequence of strings constructed by special coding rules [39]. Each element in the sequence is a gene that constitutes the chromosome.  
```
Begin SEGA  Initialize the population according to the code rules  Retain the best population do  Evaluate fitness value and optimal individuals  Perform Selection  Perform Crossover on candidates with a high fitness value  Perform Mutation on candidates with a high fitness value  Update the best population with optimal individuals while (terminination threshold is not met) return the best population End SEGA
```  
**Algorithm 1**Srengthen Elitist GA Algorithm  
All the chromosomes in the population are allowed to evolve towards a given goal (the optimal solution) and to compete for survival during the evolutionary process. The laws of nature will come into play at this point where the winners in this competition will be retained, while the losers will be eliminated.  
All the winners, except for the optimal chromosome, will produce many new individuals to replace the eliminated losers by crossover and mutation (Figure 3). The total population will therefore be kept intact while allowing the best genes to be passed on, which is called elite retention [40; 41]. The elite retention method uses a reduced number of iterations to avoid a prolonged convergence time of the algorithm, which is caused by avoiding the destruction of good genes by crossover and mutation. Increasing the genetic diversity of the pool by a gene mutation is more likely to generate new optimal chromosomes and is less likely to cause the algorithm to fall into local optimal solutions. The elite retention method that is used by the SEGA algorithm may, however, cause the algorithm to fall into a local optimal solution after only a few iterations. This problem is avoided by setting larger crossover and mutation probabilities in the SEGA algorithm.  
After each round of chromosome competition for survival, genetic recombination, and mutation, the new chromosome population, containing the best genes, constitutes the \"next generation\". This generation will continue to evolve toward the final goal of survival and compete again. Each generation is the offspring of the winner, therefore the new generation will be stronger than the previous one, and the population will gradually advance toward the set goal.