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The canonical example, of course, is the many varieties of domesticated dogs (breeds as diverse as bulldogs, chihuahuas and dachshunds have been produced from wolves in only a few thousand years), but less well-known examples include cultivated maize (very different from its wild relatives, none of which have the familiar "ears" of human-grown corn), goldfish (like dogs, we have bred varieties that look dramatically different from the wild type), and dairy cows (with immense udders far larger than would be required just for nourishing offspring).Critics might charge that creationists can explain these things without recourse to evolution.Again these winning individuals are selected and copied over into the next generation with random changes, and the process repeats.
Evolution is now producing practical benefits in a very different field, and this time, the creationists cannot claim that their explanation fits the facts just as well.In a pool of randomly generated candidates, of course, most will not work at all, and these will be deleted.However, purely by chance, a few may hold promise - they may show activity, even if only weak and imperfect activity, toward solving the problem.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.