def execute_iterated(self, board, genetic_steps, hierarchical_steps = 10):
first_boards = fill_steps(board, steps = 3)
top_solution_boards = first_boards
for i in range(hierarchical_steps):
boards = top_solution_boards[:self.population_size]
population = []
for b in boards:
population.extend(generate_population(b, self.population_size // len(boards)))
population.extend(generate_population(boards[-1], self.population_size % len(boards)))
(output_population, solution) = self.genetic.execute(population=population, steps=genetic_steps)
if solution != None:
return (output_population, solution)
sort_by_fitness = lambda p: p.fitness if p.fitness is not None else 100
top_solutions = sorted(output_population, key=sort_by_fitness, reverse = True)[:50]
top_solutions_boards = [g.board.copy() for g in top_solutions]
# Leave just the invariants
for b in top_solutions_boards:
for (r, row) in enumerate(b):
for (c, col) in enumerate(row):
b[r,c] = 0
b.fix_invariants()
top_solution_boards.extend(boards)
return (output_population, solution)
def execute(self, board, genetic_steps):
boards = fill_steps(board, steps = 4)
boards = boards[:self.population_size]
population = []
for b in boards:
population.extend(generate_population(b, self.population_size // len(boards)))
population.extend(generate_population(boards[-1], self.population_size % len(boards)))
(output_population, solution) = self.genetic.execute(population=population, steps=genetic_steps)
return (output_population, solution)
