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Genetic algorithms can optimize solutions through imitation of natural selection from "summary" of Machine Learning by Stephen Marsland
Genetic algorithms are inspired by the process of natural selection, where the fittest individuals in a population are more likely to survive and pass on their genes to the next generation. In the context of optimization problems, genetic algorithms work by evolving a population of potential solutions over multiple generations to find the best solution to a given problem. At the start of the process, a population of potential solutions is generated randomly. Each individual in the population represents a possible solution to the optimization problem. These individuals are then evaluated based on a fitness function that measures how well they perform in solving the problem. The fitter individuals - those that have higher fitness scores - are more likely to be selected for reproduction. During the reproduction phase, pairs of ...
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