desdeo_emo.EAs.PPGA
Module Contents
Classes
PredatoryPrey genetic algorithm. 

The 2dimensional toroidal lattice in which the predators and prey are placed. 
 class desdeo_emo.EAs.PPGA.PPGA(problem, population_size: int = 100, population_params=None, initial_population=None, n_iterations: int = 10, n_gen_per_iter: int = 10, predator_pop_size: int = 50, prey_max_moves: int = 10, prob_prey_move: float = 0.3, offspring_place_attempts: int = 10, kill_interval: int = 7, max_rank: int = 20, neighbourhood_radius: int = 3)[source]
Bases:
desdeo_emo.EAs.BaseEA.BaseEA
PredatoryPrey genetic algorithm.
A population of prey signify the various models or solutions to the problem at hand. Weaker prey, i.e. bad models or solutions, are killed by predators. The predators and prey are placed in a lattice, in which they are free to roam.
In each generation, each predator gets a certain number of turns to move about and hunt in its neighbourhood, killing the weaker prey, according to a fitness criteria. After this, each prey gets a certain number of moves to pursue a random walk and to reproduce with other prey. Each reproduction step generates two new prey from two parents, by crossing over their attributes and adding random mutations. After each prey has completed its move, the whole process starts again.
As the weaker individuals get eliminated in each generation, the population as a whole becomes more fit, i.e. the individuals get closer to the true paretooptimal solutions.
If you have any questions about the code, please contact:
Bhupinder Saini: bhupinder.s.saini@jyu.fi Project researcher at University of Jyväskylä.
 Parameters:
population (object) – The population object
Notes
The algorithm has been created earlier in MATLAB, and this Python implementation has been using that code as a basis. See references [4] for the study during which the original MATLAB version was created. Python code has been written by Niko Rissanen under the supervision of professor Nirupam Chakraborti.
For the MATLAB implementation, see: N. Chakraborti. DataDriven BiObjective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain. In Datta, Shubhabrata, Davim, J. Paulo (eds.), Computational Approaches to Materials Design: Theoretical and Practical Aspects, pp. 346369, 2016.
References
[1] Laumanns, M., Rudolph, G., & Schwefel, H. P. (1998). A spatial predatorprey approach to multiobjective optimization: A preliminary study. In International Conference on Parallel Problem Solving from Nature (pp. 241249). Springer, Berlin, Heidelberg.
[2] Li, X. (2003). A realcoded predatorprey genetic algorithm for multiobjective optimization. In International Conference on Evolutionary MultiCriterion Optimization (pp. 207221). Springer, Berlin, Heidelberg.
[3] Chakraborti, N. (2014). Strategies for evolutionary data driven modeling in chemical and metallurgical Systems. In Applications of Metaheuristics in Process Engineering (pp. 89122). Springer, Cham.
[4] Pettersson, F., Chakraborti, N., & Saxén, H. (2007). A genetic algorithms based multiobjective neural net applied to noisy blast furnace data. Applied Soft Computing, 7(1), 387397.
 _next_gen()[source]
Run one generation of PPGA.
Intended to be used by next_iteration.
 Parameters:
population ("Population") – Population object
 select(population, max_rank=20) list [source]
Of the population, individuals lower than max_rank are selected. Return indices of selected individuals.
 Parameters:
population (Population) – Contains the current population and problem information.
max_rank (int) – Select only individuals lower than max_rank
 Returns:
List of indices of individuals to be selected.
 Return type:
list
 class desdeo_emo.EAs.PPGA.Lattice(size_x, size_y, population, predator_pop_size, target_pop_size, prob_prey_move, prey_max_moves, offspring_place_attempts, neighbourhood_radius)[source]
The 2dimensional toroidal lattice in which the predators and prey are placed.
 size_x
Width of the lattice.
 Type:
int
 size_y
Height of the lattice.
 Type:
int
 lattice
2d array for the lattice.
 Type:
ndarray
 predator_pop
The predator population.
 Type:
ndarray
 predators_loc
Location (x, y) of predators on the lattice.
 Type:
list
 preys_loc
Location (x, y) of preys on the lattice.
 Type:
list
 init_predators()[source]
Initialize the predator population, linearly distributed in [0,1] and place them in the lattice randomly.
 move_prey()[source]
Find an empty position in prey neighbourhood for the prey to move in, and choose a mate for breeding if any available.
 Returns:
mating_pop – List of parent indices to use for mating
 Return type:
list
 place_offspring(offspring)[source]
Try to place the offsprings to the lattice. If no empty spot found within number of max attempts, do not place.
 Parameters:
offspring (int) – number of offsprings
 Returns:
Successfully placed offspring indices.
 Return type:
list
 move_predator()[source]
Find an empty position in the predator neighbourhood for the predators to move in, move the predator and kill the weakest prey in its neighbourhood, if any. Repeat until > predator_max_moves.
 update_lattice(selected=None)[source]
Update prey positions in the lattice.
 Parameters:
selected (list) – Indices of preys to be removed from the lattice.
 static lattice_wrap_idx(index, lattice_shape)[source]
Returns periodic lattice index for a given iterable index.
 Parameters:
index (tuple) – one integer for each axis
lattice_shape (tuple) – the shape of the lattice to index to
 static neighbours(arr, x, y, n=3)[source]
Given a 2Darray, returns an n*n array whose “center” element is arr[x,y]
 Parameters:
arr (ndarray) – A 2Darray where to get the neighbouring cells
x (int) – X coordinate for the center element
y (int) – Y coordinate for the center element
n (int) – Radius of the neighbourhood
 Returns:
The neighbouring cells of x, y in radius n*n.
Defaults to Moore neighbourhood (n=3).