Path: blob/master/ga/__pycache__/ga.cpython-35.pyc
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Genetic Algorithm for a simple optimization problem
Parameters
----------
generation : int
number of iteration to train the algorithm
pop_size : int
number of chromosomes in the population
chromo_size : int
number of possible values (genes) per chromosome
low, high : int
lower_bound and upper_bound possible value of the randomly generated chromosome
retain_rate : float 0 ~ 1
the fraction of the best chromosome to retain. used to mate
the children for the next generation
mutate_rate : float 0 ~ 1
the probability that each chromosome will mutate
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the targeted solution
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combine the cost and chromosome into one list and sort them
in ascending order
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core method that does the crossover, mutation to generate
the possibly best children for the next generation
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mate the children by randomly choosing two parents and mix
the first half element of one parent with the later half
element of the other
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randomly change one element of the chromosome if it
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