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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.
Multiple copies are made of them, but the copies are not perfect; random changes are introduced during the copying process.
For example, creationists often explain the development of resistance to antibiotic agents in bacteria, or the changes wrought in domesticated animals by artificial selection, by presuming that God decided to create organisms in fixed groups, called "kinds" or .
Though natural microevolution or human-guided artificial selection can bring about different varieties within the originally created "dog-kind," or "cow-kind," or "bacteria-kind" (!
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.
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.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.Again these winning individuals are selected and copied over into the next generation with random changes, and the process repeats.The expectation is that the average fitness of the population will increase each round, and so by repeating this process for hundreds or thousands of rounds, very good solutions to the problem can be discovered.(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.