Improve crossover and neat parameters

genome.py

@@ -43,13 +43,13 @@ class Genome:
                 trait: ConnectionGene = random.choice([gene_map1, gene_map2])[key].copy()
                 if (not gene_map1[key].enabled) or (not gene_map2[key].enabled) and random.random() < 0.75:
                     trait.enabled = False
-            elif parent1.fitness == parent2.fitness:
-                trait: ConnectionGene = random.choice([gene_map1, gene_map2]).get(key, None)
+            elif key in gene_map1:
+                trait: ConnectionGene = gene_map1[key]
             else:
-                trait: ConnectionGene = gene_map1.get(key, None)
+                # continue
+                trait: ConnectionGene = gene_map2[key]
 
-            if trait is not None:
-                child.connection_gene_list.append(trait.copy())
+            child.connection_gene_list.append(trait.copy())
 
         return child
 
@@ -137,11 +137,10 @@ class Genome:
 
     def mutate_weight(self):
         for connection in self.connection_gene_list:
-            if random.random() < WEIGHT_CHANCE:
-                if random.random() < PERTURB_CHANCE:
-                    connection.weight += (2 * random.random() - 1) * STEPS
-                else:
-                    connection.weight = 4 * random.random() - 2
+            if random.random() < PERTURB_CHANCE:
+                connection.weight += random.gauss(0, 1)
+            else:
+                connection.weight = 4 * random.random() - 2
 
     def mutate_add_connection(self):
         self.generate_network()

neatconfig.py

@@ -1,4 +1,4 @@
-COMPATIBILITY_THRESHOLD: float = 1
+COMPATIBILITY_THRESHOLD: float = 3
 EXCESS_COEFFICIENT: float = 1
 DISJOINT_COEFFICIENT: float = 1
 WEIGHT_COEFFICIENT: float = 0.4

species.py

@@ -23,7 +23,7 @@ class Species:
 
     def remove_weak_genomes(self):
         self.genomes.sort(key=lambda genome: genome.fitness, reverse=True)
-        survive_count: int = math.ceil(len(self.genomes) / 2)
+        survive_count: int = math.ceil(len(self.genomes) / 10)
         self.genomes: List[Genome] = self.genomes[:survive_count]
 
     def get_top_genome(self) -> Genome:

xor.py

@@ -7,11 +7,12 @@ from pool import Pool
 evaluator: Evaluator = Evaluator([([i, j], [i ^ j]) for i in range(2) for j in range(2)])
 pool: Pool = Pool(300, 2, 1)
 generation: int = 1
+top_genome: Genome = None
 while True:
     pool.evaluate_fitness(evaluator.evaluate)
     top_genome: Genome = pool.get_top_genome()
-    print(f"Generation {generation} | Top Fitness: {top_genome.fitness} | Species: {len(pool.species)}")
-    if top_genome.fitness > 3.99:
+    print(f"Generation {generation} | Top Fitness: {top_genome.fitness} | Species: {len(pool.species)} | Total Genomes: {sum(len(s.genomes) for s in pool.species)}")
+    if top_genome.fitness > 3.9:
         break
     pool.breed_new_generation()
     generation += 1