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)