Changeset 1283 for framspy

Timestamp:

09/11/23 23:54:06 (15 months ago)

Author:

Maciej Komosinski

Message:

Stochastic rounding to deliver on average the expected number of mutants and crossovers per generation

File:

• framspy/evolalg/base/experiment_abc.py (modified) (3 diffs)

framspy/evolalg/base/experiment_abc.py

@@ -4 +4 @@
 import pickle
 import time
+import math
+import random
 from abc import ABC, abstractmethod
 
@@ -43 +45 @@
             ind_list.append(new_individual)
 
+    @staticmethod
+    def stochastic_round(value): # https://en.wikipedia.org/wiki/Rounding#Stochastic_rounding
+        # For example, value==2.1 should turn in most cases to int 2, rarely to int 3
+        lower = math.floor(value) # returns int
+        return lower + (random.random() < (value - lower))
+
     def make_new_population(self, individuals, prob_mut, prob_xov, tournament_size):
         """'individuals' is the input population (a list of individuals).
@@ -48 +56 @@
         Returns: a new population of size 'self.popsize' with prob_mut mutants, prob_xov offspring, and the remainder of clones."""
 
+        # if (self.popsize * probability) below is not integer (e.g. popsize=50, prob_xov=0.333, expected number of crossovers = 50*0.333=16.65), stochastic_round() will ensure that you will get on average the expected number of crossovers per generation (e.g. 16.65: less often 16, more often 17).
+        expected_mut = self.stochastic_round(self.popsize * prob_mut) # or int(...) if you accept less mutants in some cases, see the comment above
+        expected_xov = self.stochastic_round(self.popsize * prob_xov) # or int(...) if you accept less crossovers in some cases, see the comment above
+        assert expected_mut + expected_xov <= self.popsize, "If probabilities of mutation (%g) and crossover (%g) added together exceed 1.0, then the population would grow every generation..." % (prob_mut, prob_xov) # can be triggered due to stochastic_round() if prob_mut+prob_xov is close to 1 and the expected number of mutants or crossovers is not integer; if this happens, adjust popsize, prob_mut and prob_xov accordingly.
+
         newpop = []
-        expected_mut = int(self.popsize * prob_mut)
-        expected_xov = int(self.popsize * prob_xov)
-        assert expected_mut + expected_xov <= self.popsize, "If probabilities of mutation (%g) and crossover (%g) added together exceed 1.0, then the population would grow every generation..." % (prob_mut, prob_xov)
         ris = RandomIndexSequence(len(individuals))