Genetic algorithms. Simulating nature's methods of evolving the best design solution

Gradually, problem solving becoming dynamic agents interacting with the surrounding world rather than by isolated operations. Some methods are coming from nature, where organisms both cooperate and compete for environmental resources. This has led to the design of algorithms which simulate these natural processes. The genetic algorithm (GA) represents one of the most successful approaches. Genetic algorithms are adaptive search methods that simulate natural processes such as: selection, information inheritance, random mutation, and population dynamics. At first, GAs were most applicable to numerical parameter optimizations due to an easy mapping from the problem to representation space. Today they find more and more general applications thanks to: (1) understanding better the necessary properties of the required mapping, and (2) new ways to process problem constraints.<
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