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Genetic Algorithm (GA's) are a particular class of Evolutionary Algorithms (EA) that uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. These algorithms are based on the principles of natural selection and survival of the fittest, as stated by Charles Darwin in The Origin of Species. By mimicking these processes of natural selection and survival of the fittest, genetic algorithms are able to find solutions to real world problems, if they have been suitably encoded. In this book, genetic algorithms basics covering population generation, encoding, selection, crossover, mutation and iterations are discussed. Function optimization/maximization is the main point of discussion in this work. Its about implementing a chosen fitness function using different selection techniques used in Genetic algorithm and making a comparison of them based on the fitness values of function at different number of iterations. This work would be helpfulfor professionals and students/researchers who want to get an insight about understanding and implementing process of genetic algorithm for solving an optimization problem.