Genetic Algorithms and Evolutionary Computation

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Other problem-solving techniques 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 fitness function that allows each candidate to be quantitatively evaluated.

These candidates may be solutions already known to work, with the aim of the GA being to improve them, but more often they are generated at random. The GA then evaluates each candidate according to the fitness function. In a pool of randomly generated candidates, of course, most will not work at all, and these will be deleted.

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Genetic Algorithms and Evolutionary Computation

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A neural network consists of layers of processing units called nodes joined by directional links:

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Information gain is maximized when the distribution is even. Imagine a very extreme biased form of this where everyone answers the same way to a question, and that question is repeated 8 times with different wordings. You will only get 1 distinct personality type.

An initial pattern of input is presented to the input layer of the neural network, and nodes that are stimulated then transmit a signal to the nodes of the next layer to which they are connected.

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A given set of coordinates on that landscape represents one particular solution.

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