def _evaluatePopulation(self):
        hoFtournSize = min(self.generation, int(self.tournamentSize * self.hallOfFameEvaluation))
        tournSize = self.tournamentSize - hoFtournSize
        if self.useSharedSampling:
            opponents = self._sharedSampling(tournSize, self.pop, self.oldPops[-1])
        else:
            opponents = self.pop
        if len(opponents) < tournSize:
            tournSize = len(opponents)
        self._doTournament(self.pop, opponents, tournSize)
        if hoFtournSize > 0:
            hoF = list(set(self.hallOfFame))
            self._doTournament(self.pop, hoF, hoFtournSize)
        fitnesses = []
        for p in self.pop:
            fit = 0
            for opp in opponents:
                fit += self._beats(p, opp)
            if hoFtournSize > 0:
                for opp in hoF:
                    fit += self._beats(p, opp)
            if self.absEvalProportion > 0 and self.absEvaluator != None:
                fit = (1 - self.absEvalProportion) * fit + self.absEvalProportion * self.absEvaluator(p)
            fitnesses.append(fit)
        return fitnesses
dataset/ETHPy150Open pybrain/pybrain/pybrain/optimization/populationbased/coevolution/coevolution.py/Coevolution._evaluatePopulation/original