Abstract
Genetic algorithms are stochastic search algorithms which act on a population of possible solutions. They are loosely based on the mechanics of population genetics and selection. The potential solutions are encoded as ‘genes’ — strings of characters from some alphabet. New solutions can be produced by ‘mutating’ members of the current population, and by ‘mating’ two solutions together to form a new solution. The better solutions are selected to breed and mutate and the worse ones are discarded. They are probabilistic search methods; this means that the states which they explore are not determined solely by the properties of the problems. A random process helps to guide the search. Genetic algorithms are used in artificial intelligence like other search algorithms are used in artificial intelligence — to search a space of potential solutions to find one which solves the problem.
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