6. Genetic Algorithm

In general, genetic algorithm (GA) is more suitable for optimization (maximizing and minimizing) problems in numerical and random sequences with much better solutions in a short time than many other mathematical modeling alternatives. The first historical appearance of decimal number system is explained in detail. The application of GA mutation and crossover operations depend on random variability, so different random selections are conveniently explained to GA procedures. The major components of GA procedure are optimization, error minimization, fitness, target function, initial population, mutation, and crossover procedures. Although there are random elements in the method itself, GA can reach the absolute optimization solution in the shortest time. The GA method is easy to understand by everyone as it can reach the result with only arithmetic calculations without requiring detailed and heavy mathematics. However, for this, it is necessary to explain the verbal aspects of the subject within the framework of the rules of philosophy, logic, and rationality. In this chapter, the principles, logic, and similarities of GA philosophy with other classical methods are explained, and the reader ambition for the subject and self-development principles is taken into consideration by giving the necessary clues. Different application examples are given with numerical applications.