# Create parent population by keeping top X% (top_selection)
top_num = round(top_selection*N_pop)
# parent_indices = [i for i,e in enumerate(fitness_ordered_ind) if e <= top_num]
parent_indices = fitness_ordered_ind[:top_num]
parent_population = population[parent_indices, :]
num_children = len(population) - len(parent_population)
children_pop = np.zeros(shape=(num_children,player_char_num + team_char_num + schedule_char_num))
# Create Children
for c in range(num_children):
parent1 = parent_population[np.random.randint(len(parent_population), size=1)]
parent2 = parent_population[np.random.randint(len(parent_population), size=1)]
children_pop[c,:] = GA_Funs.get_child(parent1, parent2)
# Mutate Children
children_pop[c,:] = GA_Funs.mutate_individual(children_pop[c,:], mutation_p, lower_bounds, upper_bounds)
# Combine children and parents
population = np.vstack((parent_population, children_pop))
# Recheck Fitness
for n in range(N_pop):
fitness[n] = GA_Funs.get_fitness(population[n,], schedule_data,
team_data1, team_data2, team_cols1, team_cols2,
player_data1, player_data2, player_cols1, player_cols2, health_data)
# Save max fitness
fitness_plot_vals[g] = max(fitness)
print(max(fitness))
# Calculate timing
parent_indices = fitness_ordered_ind[:top_num]
parent_population = population[parent_indices, :]
num_children = len(population) - len(parent_population)
children_pop = np.zeros(shape=(num_children, player_char_num +
team_char_num + schedule_char_num))
# Create Children
for c in range(num_children):
parent1 = parent_population[np.random.randint(len(parent_population),
size=1)]
parent2 = parent_population[np.random.randint(len(parent_population),
size=1)]
children_pop[c, :] = GA_Funs.get_child(parent1, parent2)
# Mutate Children
children_pop[c, :] = GA_Funs.mutate_individual(children_pop[c, :],
mutation_p,
lower_bounds,
upper_bounds)
# Combine children and parents
population = np.vstack((parent_population, children_pop))
# Recheck Fitness
for n in range(N_pop):
fitness[n] = GA_Funs.get_fitness(population[n, ], schedule_data,
team_data1, team_data2, team_cols1,
team_cols2, player_data1,
player_data2, player_cols1,
player_cols2, health_data)
# Save max fitness
fitness_plot_vals[g] = max(fitness)
