In 1975, Holland proposed a genetic algorithm which is a stochastic algorithm based on evolution for optimization. This algorithm follows the theory of the “survival of the fittest” proposed by Charles Darwin. First, a solution population called chromosomes is initialized. It represents the problem in the bit vector form. Then the fitness of every chromosome is evaluated by use of a suitable problem’s fitness function. The selection of best chromosomes depends on their fitness and the best selected chromosomes are sent into the mating pool, which go through crossover and mutation operations to give rise to a new set of improved solutions. Genetic algorithm gives good solution in complex search spaces where traditional methods fail. It has drawbacks that it is complicated to operate on dynamic datasets and not suitable for solving constraint problems of optimization.
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