def crossover_fitness_driven_one_point(p1, p2):
point = random.randint(1, len(p1.gene_list) - 1)
c1, c2 = copy.deepcopy(p1.gene_list), copy.deepcopy(p2.gene_list)
c1[point:], c2[point:] = p2.gene_list[point:], p1.gene_list[point:]
child1 = Individual(c1)
child2 = Individual(c2)
candidates = [child1, child2, p1, p2]
best = sorted(candidates, key=lambda ind: ind.fitness, reverse=True)
return best[0:2]
def mutation_fitness_driven_bit_flip(ind, max_tries=3):
for t in range(0, max_tries):
mut = copy.deepcopy(ind.gene_list)
pos = random.randint(0, len(ind.gene_list) - 1)
g1 = mut[pos]
mut[pos] = (g1 + 1) % 2
mutated = Individual(mut)
if mutated.fitness > ind.fitness:
return mutated
return ind
def mutate(ind):
if random.random() < .5:
mut = mutation_bit_flip_ones(ind.gene_list)
else:
mut = mutation_shift_one(ind)
return Individual(mut)
def crossover(parent1, parent2):
child1_genes, child2_genes = crossover_n_point(parent1.gene_list, parent2.gene_list, 3)
return Individual(child1_genes), Individual(child2_genes)
Square(SquareType.city, needs_coverage = True, tower_cost = 200): 1
}
landscape = generate_random_landscape(list(square_grid.keys()), square_grid, rows, cols)
def fitness_function(coords):
global landscape, radar_radius
test_landscape = copy.deepcopy(landscape)
test_landscape.add_radars(coords, radar_radius)
return - test_landscape.uncovered_count() * 500 - test_landscape.radar_cost()
Individual.rows = rows
Individual.cols = cols
Individual.set_fitness_function(fitness_function)
POPULATION_SIZE = 60
CROSSOVER_PROBABILITY = .5
MUTATION_PROBABILITY = .5
MAX_GENERATIONS = 400
first_population = [Individual.generate_random(.005) for _ in range(POPULATION_SIZE)]
best_ind = random.choice(first_population)
fit_avg = []
fit_best = []
generation_num = 0
population = first_population.copy()
generation_number = 0
while generation_num < MAX_GENERATIONS:
def mutate(ind):
mut = mutation_bit_flip(ind.gene_list)
return Individual(mut)
square_grid = {
Square(SquareType.water, needs_coverage = False, tower_cost = 500): 20,
Square(SquareType.land, needs_coverage = False, tower_cost = 30): 100,
Square(SquareType.hill, needs_coverage = False, tower_cost = 100): 8,
Square(SquareType.city, needs_coverage = True, tower_cost = 200): 1
}
landscape = generate_random_landscape(list(square_grid.keys()), square_grid, rows, cols)
plot_landscape(landscape)
plot_costs(landscape)
plot_coverage(landscape)
def fintess_function(coords):
return 0
Individual.set_fitness_function(fintess_function)
Individual.rows = rows
Individual.cols = cols
ind = Individual.generate_random(.0005)
test_landscape = copy.deepcopy(landscape)
test_landscape.add_radars(ind.get_coordinates(), 7)
plot_coverage(test_landscape)
radars = ind.count_radars()
uncovered = test_landscape.uncovered_count()
print(f'Radars: {radars}')
print(f'Uncovered Squares: {uncovered}')