What Are The Different Types Of Genetic Algorithms? How Are They Used?

1. Introduction Optimization techniques are the techniques used to discover the best solution out of all the possible solutions available under the constraints present. The genetic algorithm is one such optimization algorithm built based on the natural evolutionary process of our nature. The idea of Natural Selection and Genetic Inheritance is used here. Unlike other algorithms, it uses guided random search, i.e., finding the optimal solution by starting with a random initial cost function and then searching only in the space with the least cost (in the guided direction). Suitable when you are working with huge and complex datasets. In this article, we will discuss Genetic algorithms and type of genetic algorithms [1]. 2. Genetic algorithm The genetic algorithm is based on the genetic structure and behavior of the chromosome of the population. The following things are the foundation of genetic algorithms (as shown in figure 1. Genetic algorithm). Each chromosome indicates a possible solution. Thus, the population is a collection of chromosomes. A fitness function characterizes everyone in the population. Therefore, greater fitness better is the solution. Out of the available individuals in the population, the best individuals are used to reproduce the next generation offsprings. The offspring produced