The correct answer is: Both Mutation & Cross-over.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by natural selection and genetics to search for optimal solutions to problems. In a genetic algorithm, a population of candidate solutions (called individuals) is evolved over successive generations. Each individual in the population is represented as a chromosome, which is a string of genes. The fitness of each individual is evaluated, and the fittest individuals are selected to be parents of the next generation. The parents are then recombined using genetic operators such as crossover and mutation to create new individuals. This process is repeated until a satisfactory solution is found.
Crossover is a genetic operator that takes two parents and creates two offspring by exchanging parts of their chromosomes. This can be done in a number of ways, but the most common is to randomly select a point in each chromosome and then swap the genes between the two parents. This can help to introduce new genetic material into the population and to improve the fitness of the offspring.
Mutation is a genetic operator that randomly changes one or more genes in an individual. This can help to prevent the population from becoming too inbred and to introduce new genetic material into the population. Mutation is usually applied to a small percentage of the population in each generation.
Both crossover and mutation are important genetic operators that help to improve the fitness of the population over successive generations.