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@24spiders
Description
As mentioned, I have constructed an instance of PyGAD along with a custom fitness function. However, PyGAD does not return the best solution - that is, I have modified my fitness function to print the loss value every time it is called. While it iterates, I can see it return values such as follows:
Solution 8, value [16. 0.24717325 0.4 ], dist 75884.1558043205
Where 'dist' is what should be minimized. These 3 parameters result in a fairly low value of dist. However, once PyGAD completes and exits, the result is output:
Optimized Parameters: [27. 0.24717325 0.4 ] Loss Value: 5777062.193619523
This loss value is obviously larger than the one it found with solution 8 - I am wondering why it is returning an answer with such a large loss value?
Initialization:
# Execute GA import pygad num_generations = 10 num_parents_mating = 4 mutation_rate = 0.1 num_genes = 3 # Number of parameters to optimize sol_per_pop = 10 # Population size initial_population = [ [np.random.uniform(low=3, high=40), np.random.uniform(low=0.1, high=0.4), np.random.uniform(low=0, high=1),] for i in range(num_generations)] ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=calc_loss, mutation_percent_genes=mutation_rate, gene_space = [range(3,40), [0.1,0.2,0.3,0.4], [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]], parallel_processing = 16,)