def pop(atoms):
i1 = Individual(Genome())
i1.error_vector = np.array([0, 20, 0]) # 20
i2 = Individual(Genome())
i2.error_vector = np.array([3, 3, 3]) # 9
i3 = Individual(Genome())
i3.error_vector = np.array([1, 2, 3]) # 6
i4 = Individual(Genome())
i4.error_vector = np.array([4, 3, 5]) # 12
return Population([i1, i2, i3, i4])
def test_add_evaluated(self, partially_evaluated_pop,
simple_program_signature):
i = Individual(Genome([]), simple_program_signature)
i.error_vector = np.array([0, 1, 0])
partially_evaluated_pop.add(i)
assert len(partially_evaluated_pop) == 5
assert len(partially_evaluated_pop.evaluated) == 3
def run(self):
"""Run the algorithm until termination."""
while self._full_step():
if self.generation >= self.config.max_generations:
break
verbose_solution = self.config.verbosity_config.solution_found
if verbose_solution:
if self._is_solved():
verbose_solution("Unsimplified solution found.")
else:
verbose_solution("No unsimplified solution found.")
# Simplify the best individual for a better generalization and interpreteation.
simplifier = GenomeSimplifier(self.config.evaluator,
self.config.verbosity_config)
simp_genome, simp_error_vector = simplifier.simplify(
self.best_seen.genome, self.best_seen.error_vector,
self.config.simplification_steps)
simplified_best = Individual(simp_genome)
simplified_best.error_vector = simp_error_vector
if verbose_solution:
if self._is_solved():
verbose_solution("Simplified solution found.")
else:
verbose_solution("No simplified solution found.")
return simplified_best
def run(self):
"""Run the algorithm until termination."""
while self._full_step():
if self.generation >= self.config.max_generations:
break
verbose_solution = self.config.verbosity_config.solution_found
if verbose_solution >= self.config.verbosity_config.log_level:
if self._is_solved():
log(verbose_solution, "Unsimplified solution found.")
else:
log(verbose_solution, "No unsimplified solution found.")
# Simplify the best individual for a better generalization and interpreteation.
simplifier = GenomeSimplifier(self.config.evaluator, self.config.verbosity_config)
simp_genome, simp_error_vector = simplifier.simplify(
self.best_seen.genome,
self.best_seen.error_vector,
self.config.simplification_steps
)
simplified_best = Individual(simp_genome)
simplified_best.error_vector = simp_error_vector
if verbose_solution >= self.config.verbosity_config.log_level:
if self._is_solved():
log(verbose_solution, "Simplified solution found.")
else:
log(verbose_solution, "No simplified solution found.")
return simplified_best
def step(self):
"""Perform one generation, or step, of the Simulated Annealing.
The step method assumes an evaluated Population one Individual and
produces a single candidate Individual. If the candidate individual
passes the acceptance function, it becomes the Individual in the
Population.
"""
super().step()
if self._get_temp() <= 0:
return
candidate = Individual(
self.config.get_variation_op().produce(
[self.population.best().genome],
self.config.spawner
),
self.config.signature
)
candidate.error_vector = self.config.evaluator.evaluate(candidate.program)
acceptance_probability = self._acceptance(candidate.total_error)
if np.random.random() < acceptance_probability:
self.population = Population().add(candidate)
def population(atoms, push_config):
gn = Genome()
sig = ProgramSignature(arity=0, output_stacks=[], push_config=push_config)
i1 = Individual(gn, sig)
i1.error_vector = np.array([0, 20, 0]) # 20
i2 = Individual(gn, sig)
i2.error_vector = np.array([3, 3, 3]) # 9
i3 = Individual(gn, sig)
i3.error_vector = np.array([1, 2, 3]) # 6
i4 = Individual(gn, sig)
i4.error_vector = np.array([4, 3, 5]) # 12
return Population([i1, i2, i3, i4])
def run(self) -> Individual:
"""Run the algorithm until termination."""
while self._full_step():
if self.generation >= self.config.max_generations:
break
# Simplify the best individual for a better generalization and interpretation.
simplifier = GenomeSimplifier(self.config.evaluator,
self.config.signature)
simp_genome, simp_error_vector = simplifier.simplify(
self.best_seen.genome, self.best_seen.error_vector,
self.config.simplification_steps)
simplified_best = Individual(simp_genome, self.config.signature)
simplified_best.error_vector = simp_error_vector
self.best_seen = simplified_best
return self.best_seen
def step(self):
"""Perform one generation, or step, of the Simulated Annealing.
The step method assumes an evaluated Population one Individual and
produces a single candidate Individual. If the candidate individual
passes the acceptance function, it becomes the Individual in the
Population.
"""
if self._get_temp() <= 0:
return
candidate = Individual(
self.config.get_variation_op().produce(
[self.population.best().genome],
self.config.spawner
)
)
candidate.error_vector = self.config.evaluator.evaluate(candidate.program)
acceptance_probability = self._acceptance(candidate.total_error)
if np.random.random() < acceptance_probability:
self.population = Population().add(candidate)
def test_best(self, partially_evaluated_pop, simple_program_signature):
i = Individual(Genome([]), simple_program_signature)
i.error_vector = np.array([0, 1, 0])
partially_evaluated_pop.add(i)
assert partially_evaluated_pop.best().total_error == 0.0
def test_best(self, partially_evaluated_pop):
i = Individual(Genome([]))
i.error_vector = np.array([0, 1, 0])
partially_evaluated_pop.add(i)
assert partially_evaluated_pop.best().total_error == 0.0
def test_add_evaluated(self, partially_evaluated_pop):
i = Individual(Genome([]))
i.error_vector = np.array([0, 1, 0])
partially_evaluated_pop.add(i)
assert len(partially_evaluated_pop) == 5
assert len(partially_evaluated_pop.evaluated) == 3
def _eval_indiv(indiv: Individual, evalr: Evaluator, ):
indiv.error_vector = evalr.evaluate(indiv.program)
return indiv
def test_best(self, partially_evaluated_pop):
i = Individual(Genome([]))
i.error_vector = np.array([0, 1, 0])
partially_evaluated_pop.add(i)
assert partially_evaluated_pop.best().total_error == 0.0
def test_add_evaluated(self, partially_evaluated_pop):
i = Individual(Genome([]))
i.error_vector = np.array([0, 1, 0])
partially_evaluated_pop.add(i)
assert len(partially_evaluated_pop) == 5
assert len(partially_evaluated_pop.evaluated) == 3