Artificial Ant Problem

The Artificial Ant problem is a more sophisticated yet classical GP problem, in which the evolved individuals have to control an artificial ant so that it can eat all the food located in a given environment. This example shows how DEAP can easily deal with more complex problems, including an intricate system of functions and resources (including a small simulator).

For more information about this problem, see Reference.

Primitives set used

We use the standard primitives set for the Artificial Ant problem :

        self.matrix_col = len(self.matrix[0])
        self.matrix_exc = copy.deepcopy(self.matrix)

ant = AntSimulator(600)

pset = gp.PrimitiveSet("MAIN", 0)
  • if_food_ahead is a primitive which executes its first argument if there is food in front of the ant; else, it executes its second argument.
  • prog2() and prog3() are the equivalent of the lisp PROGN2 and PROGN3 functions. They execute their children in order, from the first to the last. For instance, prog2 will first execute its first argument, then its second.
  • move_forward() makes the artificial ant move one front. This is a terminal.
  • turn_right() and turn_left() makes the artificial ant turning clockwise and counter-clockwise, without changing its position. Those are also terminals.


There is no external input as in symbolic regression or parity.

Although those functions are obviously not already built-in in Python, it is very easy to define them :

def progn(*args):
    for arg in args:

def prog2(out1, out2):
    return partial(progn, out1, out2)

def prog3(out1, out2, out3):
    return partial(progn, out1, out2, out3)

    def sense_food(self):
        ahead_row = (self.row + self.dir_row[self.dir]) % self.matrix_row

Partial functions are a powerful feature of Python which allow to create functions on the fly. For more detailed information, please refer to the Python documentation of functools.partial().

Evaluation function

The evaluation function use an instance of a simulator class to evaluate the individual. Each individual is given 600 moves on the simulator map (obtained from an external file). The fitness of each individual corresponds to the number of pieces of food picked up. In this example, we are using a classical trail, the Santa Fe trail, in which there is 89 pieces of food. Therefore, a perfect individual would achieve a fitness of 89.

def evalArtificialAnt(individual):
    # Transform the tree expression to functional Python code
    routine = gp.compile(individual, pset)
    # Run the generated routine
    return ant.eaten,

Where ant is the instance of the simulator used. The evaluate() function is a convenience one provided by DEAP and returning an executable Python program from a GP individual and its primitives function set.

Complete example

Except for the simulator code (about 75 lines), the code does not fundamentally differ from the Symbolic Regression example. Note that as the problem is harder, improving the selection pressure by increasing the size of the tournament to 7 allows to achieve better performance.

The complete examples/%sgp/ant


John R. Koza, “Genetic Programming I: On the Programming of Computers by Means of Natural Selection”, MIT Press, 1992, pages 147-161.