Genetic Algorithms
Genetic Algorithms belong to the class of evolutionary algorithm which generates solutions
to optimization problems using techniques inspired by natural evolution, such as inheritance,
mutation, selection and crossover.
A population of chromosomes is created and evaluated by the cost function, with the "most fit"
chromosomes being kept in the population while the "least fit" ones are discarded. The chromosomes are
then paired so they can mate, this involves combining portions of each chromosome to produce new chromosomes.
During the mating process random mutations are often used. The new chromosomes are evaluated by the cost function
and the process iterates in order to get closer and closer to the best solution.
iPrevent's Genetic Algorithms are used in the fields/attributes selection and combined with iPrevent's Neural
Networks in the task of weight as well as architecture optimization.
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