def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
generator = generators.strategize(generator)
evaluator = self._internal_evaluator(evaluator)
# Strategize any seeds that are passed.
strategy_seeds = None
if seeds is not None:
strategy_seeds = []
for candidate in seeds:
n = len(candidate)
c = copy.copy(candidate)
c.extend([self._random.random() for _ in range(n)])
strategy_seeds.append(c)
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, strategy_seeds, maximize, bounder, **args)
def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
generator = generators.strategize(generator)
evaluator = self._internal_evaluator(evaluator)
# Strategize any seeds that are passed.
strategy_seeds = None
if seeds is not None:
strategy_seeds = []
for candidate in seeds:
n = len(candidate)
c = copy.copy(candidate)
c.extend([self._random.random() for _ in range(n)])
strategy_seeds.append(c)
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, strategy_seeds, maximize, bounder, **args)