Changeset 1290

Timestamp:

01/15/24 05:45:10 (4 months ago)

Author:

Maciej Komosinski

Message:

Better mutation, crossover, and evaluation function for a simple minimalistic numerical optimization example

Location:

framspy/evolalg/numerical_example

Files:

• numerical_example.py (modified) (2 diffs)
• numerical_islands_example.py (modified) (1 diff)

framspy/evolalg/numerical_example/numerical_example.py

@@ -2 +2 @@
 
 from ..base.experiment_abc import ExperimentABC
-from ..structures.hall_of_fame import HallOfFame
 
 
@@ -11 +10 @@
                                save_only_best=save_only_best)
 
-    def mutate(self, gen1):
-        return list(gen1 + np.random.randint(-10, 10, len(gen1)))
+    def mutate(self, gen):
+        return gen + np.random.normal(0, 15, len(gen))
 
     def cross_over(self, gen1, gen2):
-        return gen1
+        a = np.random.uniform()
+        return a * gen1 + (1.0-a) * gen2
 
-    def evaluate(self, genotype):
-        return 1/sum([x*x for x in genotype])
+    def evaluate(self, gen):
+        return -sum([x*x for x in gen])

framspy/evolalg/numerical_example/numerical_islands_example.py

@@ -13 +13 @@
                                 save_only_best=save_only_best)
 
-    def mutate(self, gen1):
-        return gen1 + np.random.randint(-10, 10, len(gen1))
+    def mutate(self, gen):
+        return gen + np.random.normal(0, 15, len(gen))
 
     def cross_over(self, gen1, gen2):
-        return gen1
+        a = np.random.uniform()
+        return a * gen1 + (1.0-a) * gen2
 
-    def evaluate(self, genotype):
-        return 1/sum([x*x for x in genotype])
+    def evaluate(self, gen):
+        return -sum([x*x for x in gen])