def evaluationFunction(self, currentGameState, action):
        """
        Design a better evaluation function here.

        The evaluation function takes in the current and proposed successor
        GameStates (pacman.py) and returns a number, where higher numbers are better.

        The code below extracts some useful information from the state, like the
        remaining food (newFood) and Pacman position after moving (newPos).
        newScaredTimes holds the number of moves that each ghost will remain
        scared because of Pacman having eaten a power pellet.

        Print out these variables to see what you're getting, then combine them
        to create a masterful evaluation function.
        """
        # Useful information you can extract from a GameState (pacman.py)
        successorGameState = currentGameState.generatePacmanSuccessor(action)
        newPos = successorGameState.getPacmanPosition()
        newFood = successorGameState.getFood()
        newGhostStates = successorGameState.getGhostStates()
        newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]


        "*** YOUR CODE HERE ***"

        score = successorGameState.getScore()


        gDist = 100000000
        k = len (newGhostStates)

        for gh in newGhostStates:
            ghostPosition = gh.configuration.pos
            d = manhattanDistance( ghostPosition, newPos)
            if d < gDist:
                gDist = d
                if d <= 1.0:
                    score-=3


        n = len (newGhostStates)
        fDist = n * 100000

        for fo in newFood.asList():
            d = manhattanDistance( fo, newPos)
            fDists +=d

            if d < fDist and d > 0:
                fDist = d

        if(fDist == 0):
            score +=2
        else:
            score+= 1.5/fDist

        return score