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These promising candidates are kept and allowed to reproduce.

Multiple copies are made of them, but the copies are not perfect; random changes are introduced during the copying process.

These digital offspring then go on to the next generation, forming a new pool of candidate solutions, and are subjected to a second round of fitness evaluation.

Those candidate solutions which were worsened, or made no better, by the changes to their code are again deleted; but again, purely by chance, the random variations introduced into the population may have improved some individuals, making them into better, more complete or more efficient solutions to the problem at hand.

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.

There are numerous natural phenomena for which evolution gives us a sound theoretical underpinning.

To name just one, the observed development of resistance - to insecticides in crop pests, to antibiotics in bacteria, to chemotherapy in cancer cells, and to anti-retroviral drugs in viruses such as HIV - is a straightforward consequence of the laws of mutation and selection, and understanding these principles has helped us to craft strategies for dealing with these harmful organisms.

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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.

), no amount of time or genetic change can transform one "kind" into another.

However, exactly how the creationists determine what a "kind" is, or what mechanism prevents living things from evolving beyond its boundaries, is invariably never explained.

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.

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