Genetic-Algorithm

providing genetic algorithm for problems

this algorithm is now in developing mode

and it solves:

finding extermum value of the specific funtion

How to Use:

in tools.py

• get_bin_value() // getting the binary value
• map_binary_value_to_domain() //maping the value of the binary array to the range of the domain
• find_bits_for_accurct() //finiding needs bit for genes in chromosome
• convert_binary_to_gray() //converting binay code to gray code
• conver_gray_to_binary() //converting gray code to binary code
• choice_zero_or_one() //random 0 or 1 value
• choice_int_between_a_and_b() //random int value between [a,b]
• choice_float_between_zero_and_one() //float random value between 0 or 1
• single_point_crossover() //crossover opration
• calculate_roullet_wheel() //random selection
• calculate_searching_space_accurcy() //calculate accuracy of the defined space

in scaling_tool.py:

we provide some scaling tool for scaling the fitness function value

• linear_scaling()
• dynamic_linear_scaling
• pow_scaling
• logarithm_scaling
• nomalization

in GA.py: we defined main class

• Chromosome:
• Generation:
• Genetic:

for using use custom GA algoritm you can have your main() methon in Genetic class

the parameter that you can tune it:

• number_of_papulation //Number of the papulation for starting
• function //your specific function
• fitness_function //custom fitness function
• domain //input space
• mutation_rate //mutation rate(default is 0)
• crossover_rate//crossover rate (default is 1)
• mutation_bit //number of genes bit that you want to do the mutation oprand
• accurcy //accuracy of the genes value