# these practical asked in exam
import pygad
import numpy as np
X=[4,-2,3.5,5,-11,-4.7]
desired_output=44
def fitness_function(ga_instance, solution, solution_idx):
output=np.sum(solution*X)# Output is the predicted output
fitness=1.0/np.abs(output-desired_output) # we are trying to find error #
fitness is reciprocal of error because we want to get a higher value return fitness
return fitness
# ga_instance is a genetic algorithm instance
# solution
# Solution_index because there will be solutions for different generations,
ga_instance=pygad.GA(num_generations=50, #it will run 50 times
num_parents_mating=8, # 8 parents will be used for cre
fitness_func=fitness_function,
sol_per_pop=8,# Solution_per_population
# In each generation we will consider 8 childs as pare
num_genes=len(X),
init_range_low=-2,
init_range_high=5,
parent_selection_type="sss", #sss = Steady Selection St
#It is for maintaining the same number of parents in e
keep_parents=1, # Means that 1 parent from previous gen
crossover_type="single_point",
mutation_type="random",
mutation_percent_genes=10
)
ga_instance.run()
solution, solution_fitness, solution_idx=ga_instance.best_solution()
print("solution = ", solution)
print("Fitness = ", solution_fitness)
print("solution IDX = ", solution_idx)
