add: demo genetic algorithm

genetic_alg.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super(NeuralNetwork, self).__init__()
+        self.hidden = nn.Linear(5, 10)
+        self.output = nn.Linear(10, 3)
+
+    def forward(self, x):
+        x = torch.relu(self.hidden(x))
+        x = self.output(x)
+        return torch.log_softmax(x)
+
+class GeneticAlgorithm:
+    def __init__(self, populationSize: int, mutationRate: float, percentageBest):
+        self.populationSize = populationSize
+        self.mutationRate = mutationRate
+        self.percentageBest = percentageBest
+        self.initialize_population()
+
+    def initialize_population(self):
+        self.population = [NeuralNetwork() for _ in range(self.populationSize)]
+    
+    def crossover(self, parent1: NeuralNetwork, parent2: NeuralNetwork):
+        child1 = NeuralNetwork()
+        child2 = NeuralNetwork()
+        point = len(child1.hidden.weight.data) // 2
+        print([x for x in child1.parameters()])
+        child1.hidden.weight.data = torch.cat((parent1.hidden.weight.data[:point], parent2.hidden.weight.data[point:]), dim=0)
+        child2.hidden.weight.data = torch.cat((parent2.hidden.weight.data[:point], parent1.hidden.weight.data[point:]), dim=0)
+        child1.output.weight.data = parent1.output.weight.data.clone().detach()
+        child2.output.weight.data = parent2.output.weight.data.clone().detach()
+        return child1, child2
+
+    # Mutation operator: Random mutation
+    def mutate(self, model: NeuralNetwork):
+        for param in model.parameters():
+            if torch.rand(1).item() < self.mutationRate:
+                param.data += torch.randn_like(param.data) * 0.1
+        return model
+
+    def learn(self, fitness: list):
+        self.population = [self.population[x] for x in np.argsort(fitness)]
+        numBest = int(self.populationSize * self.percentageBest)
+        self.population = self.population[:numBest]
+        while len(self.population) < self.populationSize:
+            parent1, parent2 = np.random.choice(self.population), np.random.choice(self.population)
+            child1, child2 = self.crossover(parent1, parent2)
+            child1 = self.mutate(child1)
+            child2 = self.mutate(child2)
+            self.population.extend([child1, child2])
+        while len(self.population) > self.populationSize: self.population.pop()
+    
+    def predict(self, data: list):
+        result = []
+        for i in range(self.populationSize):
+            result.append(self.population[i](data[i]))
+        return result