def _learnStep(self):
        # re-evaluate the current individual in case the evaluator is noisy
        if self.evaluatorIsNoisy:
            self.bestEvaluation = self._oneEvaluation(self.bestEvaluable)

        # hill-climbing
        challenger = self.bestEvaluable.copy()
        challenger.mutate()
        newEval = self._oneEvaluation(challenger)

        # if the new evaluation was better, it got stored automatically. Otherwise:
        if ((not self.minimize and newEval < self.bestEvaluation) or
            (self.minimize and newEval > self.bestEvaluation)):
            acceptProbability = exp(-abs(self.bestEvaluation-newEval)/self.temperature)
            if random() < acceptProbability:
                self.bestEvaluable, self.bestEvaluation = challenger, newEval
dataset/ETHPy150Open pybrain/pybrain/pybrain/optimization/hillclimber.py/StochasticHillClimber._learnStep/swapped operands of `-`