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This field is computer science, and the benefits come from a programming strategy called Concisely stated, a genetic algorithm (or GA for short) is a programming technique that mimics biological evolution as a problem-solving strategy.Given a specific problem to solve, the input to the GA is a set of potential solutions to that problem, encoded in some fashion, and a metric called a that allows each candidate to be quantitatively evaluated.One example of this technique is Hiroaki Kitano's "grammatical encoding" approach, where a GA was put to the task of evolving a simple set of rules called a context-free grammar that was in turn used to generate neural networks for a variety of problems (Mitchell 1996, p. The virtue of all three of these methods is that they make it easy to define operators that cause the random changes in the selected candidates: flip a 0 to a 1 or vice versa, add or subtract from the value of a number by a randomly chosen amount, or change one letter to another. In this approach, random changes can be brought about by changing the operator or altering the value at a given node in the tree, or replacing one subtree with another.(See the section on Methods of change for more detail about the genetic operators.) Another strategy, developed principally by John Koza of Stanford University and called , represents programs as branching data structures called trees (Koza et al. Figure 1: Three simple program trees of the kind normally used in genetic programming.As astonishing and counterintuitive as it may seem to some, genetic algorithms have proven to be an enormously powerful and successful problem-solving strategy, dramatically demonstrating the power of evolutionary principles.Genetic algorithms have been used in a wide variety of fields to evolve solutions to problems as difficult as or more difficult than those faced by human designers.The shape of a protein determines its function.) Genetic algorithms for training neural networks often use this method of encoding also.A third approach is to represent individuals in a GA as strings of letters, where each letter again stands for a specific aspect of the solution.
However, the evidence of biology alone shows that this claim is untrue.Moreover, the solutions they come up with are often more efficient, more elegant, or more complex than anything comparable a human engineer would produce.In some cases, genetic algorithms have come up with solutions that baffle the programmers who wrote the algorithms in the first place!The evolutionary postulate of common descent has aided the development of new medical drugs and techniques by giving researchers a good idea of which organisms they should experiment on to obtain results that are most likely to be relevant to humans.Finally, the principle of selective breeding has been used to great effect by humans to create customized organisms unlike anything found in nature for their own benefit.