Genetic-Algorithm

Genetic algorithm (GA) is a process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring. Typically, GA has five phases: (a) Initial population: the set of genes of an individual is represented using a string usually binary values are used (0’s and 1’s), (b) Fitness score: This tells us how fit an individual is. This score tells us the probability that an individual is selected for reproduction, (c) Selection: In this phase, the fittest individuals are considered for reproducing the offsprings, (d) Crossover: Crossover is an important part of GA. For the selected parents, a crossover point is chosen and offsprings are created, (e) Mutation: New offsprings formed are further mutated at random points and this is done to prevent premature convergence. We developed a genetic method that employs crossover and mutation to aim to produce a string of only 1's from a randomly generated bit string of length n. Throughout the experiment, several values are chosen for the population size, individual size, drop percentage, crossover percentage, and mutation percentage. Plots were created in accordance with the various fitness score values that were acquired.