Genetic Algorithms (GA) are a method of "breeding" computer programs and solutions to optimization or search problems by means of simulated evolution. Processes loosely based on natural selection, crossover, and mutation are repeatedly applied to a population of binary strings which represent potential solutions. Over time, the number of
above average individuals increases, and better fit individuals are created, until a good solution to the problem at hand is found.
GA are especially adequate for searches in large state space, multi-modal state space, or n-dimensional surface, where they may offer significant benefits over more typical search or optimization techniques.
GA have been used in adaptive systems design, adaptive control, finite automata They also play an important role in parameter specification for neural networks.
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