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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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(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.

Another, similar approach is to encode solutions as arrays of integers or decimal numbers, with each position again representing some particular aspect of the solution.

This approach allows for greater precision and complexity than the comparatively restricted method of using binary numbers only and often "is intuitively closer to the problem space" (Fleming and Purshouse 2002, p. This technique was used, for example, in the work of Steffen Schulze-Kremer, who wrote a genetic algorithm to predict the three-dimensional structure of a protein based on the sequence of amino acids that go into it (Mitchell 1996, p. Schulze-Kremer's GA used real-valued numbers to represent the so-called "torsion angles" between the peptide bonds that connect amino acids.

But in the last few decades, the continuing advance of modern technology has brought about something new.

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.

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