*3.3. Genetic Algorithm*

The genetic algorithm (GA) is an evolutionary metaheuristic algorithm that has been applied to many engineering disciplines [33,34]. GAs work with a population of entities denoted by bit strings and change the population with random exploration and competition. In general, GAs consist of trials, for example, selection, crossover, and mutation. Generation is a procedure in which a novel cluster of entities is made by choosing the proper entities in the existing population. Crossover is the greatest influential worker in GAs. It constructs novel children by choosing two strings and exchange segments of their structures. The novel children may exchange the worst entities in the population. The mutation is a local worker with a very low likelihood, where its purpose is to adjust the value of a random position in a string.

In this work, the population of the GA algorithm is 100, where the population are initialized randomly. After that, this generated population is reproduced using crossover and mutation, where the crossover probability is set to 80% and the mutation probability is set to 0.05.
