Source Code for Module Bio.GA.Selection.Tournament

 1  """Provide Tournament style selection. 
 2   
 3  This implements selection based on a tournament style. In this model of 
 4  selection, two individuals are randomly chosen from the population, and 
 5  the organism with the higher fitness is considered the 'winner' and moves 
 6  to the next generation. 
 7  """ 
 8  # standard modules 
 9  import random 
10   
11  # local modules 
12  from Abstract import AbstractSelection 
13   
14   
15 -class TournamentSelection(AbstractSelection): 
16      """Implement tournament style selection. 
17      """ 
18 -    def __init__(self, mutator, crossover, repairer, num_competitors = 2): 
19          """Initialize the tournament selector. 
20   
21          Arguments: 
22   
23          o num_competitors-- The number of individiuals that should be 
24          involved in a selection round. By default we just have two 
25          individuals (head to head!). 
26   
27          See AbstractSelection for a description of the arguments to 
28          the initializer. 
29          """ 
30          AbstractSelection.__init__(self, mutator, crossover, repairer) 
31   
32          if num_competitors < 2: 
33              raise ValueError("Must have at least 2 competitors!") 
34   
35          self._num_competitors = num_competitors 
36   
37 -    def select(self, population): 
38          """Perform selection on the population using the Tournament model. 
39   
40          Arguments: 
41   
42          o population -- A population of organisms on which we will perform 
43          selection. The individuals are assumed to have fitness values which 
44          are due to their current genome (ie. the fitness is up to date). 
45          """ 
46          # we want to create a new population of the same size as the original 
47          new_population = [] 
48   
49          while len(new_population) < len(population): 
50              # select two individuals using tournament selection 
51              new_orgs = [] 
52              # select for two individuals 
53              for round_num in range(2): 
54                  competitors = [] 
55                  while len(competitors) < self._num_competitors: 
56                      new_org = random.choice(population) 
57                      if new_org not in competitors: 
58                          competitors.append(new_org) 
59   
60                  # sort the competitors by fitness, this will put them 
61                  # from lowest to highest 
62                  competitors.sort(key = lambda org: org.fitness) 
63   
64                  # get the best organism 
65                  new_orgs.append(competitors[-1]) 
66   
67              assert len(new_orgs) == 2, "Expected two organisms to be selected" 
68   
69              # do mutation and crossover to get the new organisms 
70              new_org_1, new_org_2 = self.mutate_and_crossover(new_orgs[0], 
71                                                               new_orgs[1]) 
72   
73              new_population.extend([new_org_1, new_org_2]) 
74   
75          return new_population 
76