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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.

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(A protein is made up of a sequence of basic building blocks called amino acids, which are joined together like the links in a chain.

Once all the amino acids are linked, the protein folds up into a complex three-dimensional shape based on which amino acids attract each other and which ones repel each other.

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 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.Methods of representation Before a genetic algorithm can be put to work on any problem, a method is needed to encode potential solutions to that problem in a form that a computer can process.One common approach is to encode solutions as binary strings: sequences of 1's and 0's, where the digit at each position represents the value of some aspect of the solution.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.In a pool of randomly generated candidates, of course, most will not work at all, and these will be deleted.