Genetic algorithms are the methods of solving search-based optimization on the principles of Genetics and Natural Selection, the process that drives biological evolution. It mainly used in research, and machine learning. It finds optimal or near-optimal solutions for difficult problems which might take a lifetime to solve. Genetic algorithms are a subset of a much larger branch of computation known as Evolutionary Computation. GAs were developed by John Holland and his students and colleagues at the University of Michigan by taking inspiration of the nature. The evolution usually starts from a population of randomly generated individuals. Algorithms simulate “survival of the fittest” among individual of consecutive generation for solving a problem. These solutions are on recombination and mutation, producing new children, and the process is repeated over various generations. Each individual is assigned a fitness value and the fitter individuals are given a higher chance to mate and yield more “fitter” individuals. Genetic Algorithms are sufficiently randomized in nature, but they work much better than random local search because they exploit historical information as well. Foundation Genetic algorithms are based on this analogy - Individual in population compete for resources and mate Successful individuals are mate to create more offspring than others Fittest parent genes propagate throughout the generation, that is sometimes parents create offspring which is better than either parent. Each successive generation is more suited for their environment. Advantages Genetic algorithms have various advantages which have made them immensely popular. Here are the most notable advantages - Does not require any derivative information Very good parallel capabilities. Faster and more efficient as compared to the traditional methods. Provides more than one good solutions. Useful for searching in very large space and there are a large number of parameters involved.