Genetic Algorithms GA is a search algorithm used in computational mathematics to solve optimization problems. It is a type of evolutionary algorithm, which originally borrowed from some phenomena in evolutionary biology, including inheritance, mutation, natural selection and hybridization.

Genetic algorithms are usually implemented in the form of computer simulation. For an optimization problem, when there are a certain number of candidate solutions, they can be abstractly represented as chromosomes to allow the population to evolve towards a better solution. Usually the solution is represented in binary (i.e. a string of 0s and 1s), but it can also be represented in other ways.

Evolution originates from a completely random population of individuals and then occurs generation after generation. In each generation, the fitness of the entire population is evaluated and multiple individuals are randomly selected from the current population based on their fitness. The new population generated through natural selection and mutation will become the current population in the next iteration of the algorithm.