lec07-GA - Genetic Algorithms Genetic algorithms provide an...

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Genetic Algorithms Genetic algorithms provide an approach to learning that is based loosely on simulated evolution. Hypotheses are often described by bit strings whose interpretation depends on the application. The search for an appropriate hypothesis begins with a population of initial hypotheses. Members of the current population give rise to the next generation population by means of operations such as random mutation and crossover, which are patterned after processes in biological evolution. The hypotheses in the current population are evaluated relative to a given measure of fitness, with the most fit hypotheses selected probabilistically as seeds for producing the next generation.
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Genetic Algorithms Genetic algorithms (GAS) provide a learning method motivated by an analogy to biological evolution. GAs generate successor hypotheses by repeatedly mutating and recombining parts of the best currently known hypotheses. At each step, the current population is updated by replacing some fraction of the population by offspring of the most fit current hypotheses. The process forms a generate-and-test beam-search of hypotheses, in which variants of the best current hypotheses are most likely to be considered next.
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Genetic Algorithms GAs search a space of candidate hypotheses to identify the best hypothesis. In GAs the "best hypothesis" is defined as the one that optimizes a predefined numerical measure for the problem at hand, called the hypothesis fitness. For example, if the learning task is the problem of approximating an unknown function given training examples of its input and output, then fitness could be defined as the accuracy of the hypothesis over this training data.
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A prototypical genetic algorithm
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lec07-GA - Genetic Algorithms Genetic algorithms provide an...

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