ga_usage.py

# Construct the class
ga = GA(dna_size=2,                    # Each dna will have size elements
        dna_bounds=(-5.11, 5.11),      # DNA cannot optimise outside of these bounds
        dna_start_position=[3.1, 3.1], # DNA begins randomly around this position 
        elitism=0.5,                   # We keep the 50% fittest individuals
        population_size=500,           # We have 500 genes in the gene pool
        mutation_rate=0.01,            # Roguhly every 100 dice rolls we mutate
        mutation_sigma=0.1,            # The gene will mutated at a bound of (-0.1, 0.1)
        mutation_decay=0.999,          # The mutation size will decay each step
        mutation_limit=0.01)           # The mutation size will never get smaller than this
 
# Loop and optimise each step 
for optimisation_step in range(amount_optimisation_steps):

    # Get the current gene pool
    positions = ga.get_solutions()
    
    # Evaluate the genes using a (currently) non-existant evaluation function
    fitnesses = [evaluate(pos) for pos in positions]
    
    # Report how well each the genes performed to the
    # class, so it can internally optimise.
    ga.set_fitnesses(fitnesses)
    
    # Get the current best.
    best_position, best_fitness = ga.get_best()