Hitchhiker's Guide to AI, Software Architecture, and Everything Else: Combining Neural Networks, Swarm Intelligence, and Reinforcement Learning for Human-Like Intelligence

Introduction

Human intelligence emerges from the complex interplay of billions of neurons, collective decision-making processes, and continuous learning from experience. To replicate this computationally, we need to combine multiple AI paradigms that each capture different aspects of human cognition. This article explores how neural networks, swarm intelligence, and reinforcement learning can be integrated to create more human-like artificial intelligence systems.

Core Concepts and Detailed Explanations

Neural Networks: The Foundation of Cognitive Processing

Neural networks represent the computational backbone of our hybrid system, directly inspired by the biological neural networks in the human brain. These networks consist of interconnected nodes (neurons) that process information through weighted connections (synapses).

Key Characteristics:

Parallel Processing: Like the human brain, neural networks can process multiple pieces of information simultaneously
Pattern Recognition: Exceptional ability to identify complex patterns in data, similar to how humans recognize faces or understand speech
Learning Through Adaptation: Weights are adjusted based on experience, mimicking synaptic plasticity in biological neurons
Hierarchical Feature Extraction: Deep networks learn increasingly complex features at each layer, similar to how the visual cortex processes information

Rationale for Inclusion:

Neural networks provide the fundamental computational substrate for learning and decision-making. They excel at tasks that require pattern recognition, function approximation, and non-linear mapping - all crucial components of human intelligence. However, traditional neural networks lack the distributed problem-solving capabilities and exploration strategies that characterize human collective intelligence and individual learning behavior.

Swarm Intelligence: Collective Problem-Solving and Emergent Behavior

Swarm intelligence draws inspiration from the collective behavior of social organisms like ants, bees, birds, and fish. Despite having simple individual behaviors, these organisms exhibit sophisticated collective intelligence that emerges from their interactions.

Core Principles:

Self-Organization: Complex patterns and behaviors emerge without centralized control
Collective Decision-Making: Groups make better decisions than individuals through information aggregation
Distributed Problem-Solving: Problems are solved through the coordinated efforts of many simple agents
Adaptive Exploration: Swarms naturally balance exploration of new solutions with exploitation of known good solutions
Robustness: System continues to function even if individual agents fail

Key Algorithms:

Particle Swarm Optimization (PSO): Particles explore solution space by following personal best and global best positions
Ant Colony Optimization (ACO): Artificial ants find optimal paths by depositing and following pheromone trails
Artificial Bee Colony (ABC): Bees search for optimal solutions through employed, onlooker, and scout bee behaviors

Rationale for Inclusion:

Humans don't solve problems in isolation - we benefit from collective intelligence, social learning, and distributed cognition. Swarm intelligence provides mechanisms for exploration, optimization, and collective decision-making that complement the pattern recognition capabilities of neural networks. It addresses the exploration-exploitation dilemma that individual learners face and provides robustness through redundancy.

Reinforcement Learning: Goal-Oriented Behavior and Adaptation

Reinforcement learning (RL) models how humans and animals learn through interaction with their environment, receiving feedback in the form of rewards and punishments. This paradigm captures the goal-oriented, adaptive nature of human behavior.

Key Components:

Agent: The learner or decision-maker (analogous to a human)
Environment: The world in which the agent operates
State: Current situation or context
Action: Choices available to the agent
Reward: Feedback signal indicating the desirability of actions
Policy: Strategy for selecting actions based on states

Learning Mechanisms:

Temporal Difference Learning: Learn from the difference between predicted and actual rewards
Q-Learning: Learn the value of state-action pairs
Policy Gradient Methods: Directly optimize the policy for selecting actions
Actor-Critic Methods: Combine value estimation with policy optimization

Rationale for Inclusion:

Human intelligence is fundamentally goal-oriented and adaptive. We learn from consequences, adjust our behavior based on outcomes, and develop strategies to maximize long-term rewards. Reinforcement learning provides the framework for goal-directed behavior and continuous adaptation that neural networks alone cannot provide. It enables the system to learn not just patterns, but optimal behaviors in dynamic environments.

Integration Strategy and Architecture

Hierarchical Integration Model

The hybrid system operates on multiple levels, each leveraging different AI paradigms:

Level 1: Individual Neural Processing

- Deep neural networks handle perception, pattern recognition, and basic decision-making

- Convolutional networks for visual processing

- Recurrent networks for sequential information processing

- Attention mechanisms for focus and relevance

Level 2: Swarm-Based Exploration and Optimization

- Multiple neural network agents explore different solution strategies

- Swarm algorithms optimize network parameters and architectures

- Collective decision-making aggregates individual agent outputs

- Dynamic population management maintains diversity

Level 3: Reinforcement Learning Coordination

- RL framework coordinates overall system behavior

- Reward signals guide both individual learning and swarm dynamics

- Policy networks determine when to rely on individual vs. collective intelligence

- Meta-learning adapts the integration strategy itself

Synergistic Benefits

Enhanced Exploration: Swarm intelligence provides diverse exploration strategies that prevent neural networks from getting stuck in local optima, while RL guides exploration toward rewarding regions of the solution space.

Robust Learning: Multiple neural network agents provide redundancy and different perspectives on problems, while RL ensures the system adapts to changing environments.

Emergent Intelligence: The combination creates emergent behaviors that exceed the capabilities of any single paradigm, similar to how human intelligence emerges from the interaction of neurons, social learning, and experience.

Adaptive Specialization: Different agents can specialize in different aspects of problems while maintaining coordination through swarm mechanisms and RL-based meta-control.

Biological and Psychological Foundations

Neuroscientific Inspiration

The human brain exhibits characteristics of all three paradigms:

Neural Networks: Billions of interconnected neurons process information in parallel
Swarm Intelligence: Collective behavior emerges from local neural interactions without centralized control
Reinforcement Learning: Dopaminergic pathways provide reward signals that guide learning and behavior

Cognitive Science Connections

Human cognition involves:

Individual Processing: Pattern recognition, memory, and reasoning (neural networks)
Social Intelligence: Learning from others, collective problem-solving (swarm intelligence)
Adaptive Behavior: Goal pursuit, learning from consequences (reinforcement learning)

Implementation Challenges and Solutions

Challenge 1: Coordination Complexity

Problem: Coordinating multiple AI paradigms without interference

Solution: Hierarchical architecture with clear interfaces and communication protocols

Challenge 2: Computational Efficiency

Problem: Combined system may be computationally expensive

Solution: Adaptive resource allocation and selective activation of components

Challenge 3: Stability and Convergence

Problem: Multiple learning processes may interfere with each other

Solution: Careful tuning of learning rates and interaction strengths

Challenge 4: Scalability

Problem: System complexity grows with problem size

Solution: Modular design with distributed processing capabilities

Now, let me provide a comprehensive code example that demonstrates these concepts in action:

import numpy as np

import matplotlib.pyplot as plt

from collections import deque

import random

from typing import List, Tuple, Dict, Any

import json

# Set random seeds for reproducibility

np.random.seed(42)

random.seed(42)

class NeuralNetwork:

"""

Simple feedforward neural network with backpropagation.

Represents individual cognitive processing capabilities.

"""

def __init__(self, layers: List[int], learning_rate: float = 0.01):

self.layers = layers

self.learning_rate = learning_rate

self.weights = []

self.biases = []

# Initialize weights and biases using Xavier initialization

for i in range(len(layers) - 1):

weight_matrix = np.random.randn(layers[i], layers[i+1]) * np.sqrt(2.0 / layers[i])

bias_vector = np.zeros((1, layers[i+1]))

self.weights.append(weight_matrix)

self.biases.append(bias_vector)

def sigmoid(self, x):

"""Sigmoid activation function with numerical stability"""

return 1 / (1 + np.exp(-np.clip(x, -500, 500)))

def sigmoid_derivative(self, x):

"""Derivative of sigmoid function"""

return x * (1 - x)

def forward(self, X):

"""Forward propagation through the network"""

self.activations = [X]

current_input = X

for i, (weight, bias) in enumerate(zip(self.weights, self.biases)):

z = np.dot(current_input, weight) + bias

if i == len(self.weights) - 1: # Output layer

current_input = z # Linear activation for output

else: # Hidden layers

current_input = self.sigmoid(z)

self.activations.append(current_input)

return current_input

def backward(self, X, y, output):

"""Backpropagation algorithm for learning"""

m = X.shape[0]

# Calculate output layer error

dZ = output - y

# Backpropagate through layers

for i in reversed(range(len(self.weights))):

dW = (1/m) * np.dot(self.activations[i].T, dZ)

db = (1/m) * np.sum(dZ, axis=0, keepdims=True)

if i > 0: # Not input layer

dA_prev = np.dot(dZ, self.weights[i].T)

dZ = dA_prev * self.sigmoid_derivative(self.activations[i])

# Update weights and biases

self.weights[i] -= self.learning_rate * dW

self.biases[i] -= self.learning_rate * db

def train(self, X, y, epochs: int = 1000):

"""Train the neural network"""

losses = []

for epoch in range(epochs):

output = self.forward(X)

loss = np.mean((output - y) ** 2)

losses.append(loss)

self.backward(X, y, output)

return losses

def predict(self, X):

"""Make predictions using the trained network"""

return self.forward(X)

def get_weights_flat(self):

"""Get all weights as a flat array for optimization"""

flat_weights = []

for weight_matrix in self.weights:

flat_weights.extend(weight_matrix.flatten())

for bias_vector in self.biases:

flat_weights.extend(bias_vector.flatten())

return np.array(flat_weights)

def set_weights_from_flat(self, flat_weights):

"""Set weights from a flat array"""

idx = 0

for i, weight_matrix in enumerate(self.weights):

size = weight_matrix.size

self.weights[i] = flat_weights[idx:idx+size].reshape(weight_matrix.shape)

idx += size

for i, bias_vector in enumerate(self.biases):

size = bias_vector.size

self.biases[i] = flat_weights[idx:idx+size].reshape(bias_vector.shape)

idx += size

class SwarmAgent:

"""

Individual agent in the swarm with its own neural network.

Represents distributed problem-solving capabilities.

"""

def __init__(self, network_architecture: List[int], bounds: Tuple[float, float]):

self.network = NeuralNetwork(network_architecture)

self.position = self.network.get_weights_flat() # Current solution

self.velocity = np.random.uniform(-1, 1, len(self.position))

self.best_position = self.position.copy()

self.best_fitness = float('inf')

self.fitness = float('inf')

self.bounds = bounds

def update_velocity(self, global_best_position: np.ndarray,

w: float = 0.7, c1: float = 1.5, c2: float = 1.5):

"""

Update velocity using PSO formula.

w: inertia weight, c1: cognitive parameter, c2: social parameter

"""

r1, r2 = np.random.random(2)

# PSO velocity update equation

cognitive_component = c1 * r1 * (self.best_position - self.position)

social_component = c2 * r2 * (global_best_position - self.position)

self.velocity = (w * self.velocity +

cognitive_component +

social_component)

# Limit velocity to prevent explosion

max_velocity = 0.1 * (self.bounds[1] - self.bounds[0])

self.velocity = np.clip(self.velocity, -max_velocity, max_velocity)

def update_position(self):

"""Update position based on velocity"""

self.position += self.velocity

# Apply boundary constraints

self.position = np.clip(self.position, self.bounds[0], self.bounds[1])

# Update neural network weights

self.network.set_weights_from_flat(self.position)

def evaluate_fitness(self, X, y):

"""Evaluate fitness of current position"""

predictions = self.network.predict(X)

self.fitness = np.mean((predictions - y) ** 2) # MSE loss

# Update personal best

if self.fitness < self.best_fitness:

self.best_fitness = self.fitness

self.best_position = self.position.copy()

return self.fitness

class SwarmIntelligence:

"""

Particle Swarm Optimization for neural network training.

Implements collective intelligence and distributed optimization.

"""

def __init__(self, num_agents: int, network_architecture: List[int],

bounds: Tuple[float, float] = (-5.0, 5.0)):

self.num_agents = num_agents

self.network_architecture = network_architecture

self.bounds = bounds

# Initialize swarm agents

self.agents = [SwarmAgent(network_architecture, bounds)

for _ in range(num_agents)]

self.global_best_position = None

self.global_best_fitness = float('inf')

self.fitness_history = []

def optimize(self, X, y, iterations: int = 100):

"""

Optimize neural network weights using swarm intelligence.

Demonstrates collective problem-solving and emergent behavior.

"""

print(f"Starting swarm optimization with {self.num_agents} agents...")

for iteration in range(iterations):

# Evaluate all agents

fitnesses = []

for agent in self.agents:

fitness = agent.evaluate_fitness(X, y)

fitnesses.append(fitness)

# Update global best

if fitness < self.global_best_fitness:

self.global_best_fitness = fitness

self.global_best_position = agent.position.copy()

# Record fitness statistics

avg_fitness = np.mean(fitnesses)

self.fitness_history.append({

'iteration': iteration,

'best_fitness': self.global_best_fitness,

'avg_fitness': avg_fitness,

'diversity': np.std(fitnesses)

})

# Update velocities and positions

for agent in self.agents:

if self.global_best_position is not None:

agent.update_velocity(self.global_best_position)

agent.update_position()

# Print progress

if iteration % 20 == 0:

print(f"Iteration {iteration}: Best fitness = {self.global_best_fitness:.6f}, "

f"Avg fitness = {avg_fitness:.6f}")

print(f"Swarm optimization completed. Final best fitness: {self.global_best_fitness:.6f}")

return self.global_best_position

def get_best_network(self):

"""Return the best neural network found by the swarm"""

best_network = NeuralNetwork(self.network_architecture)

best_network.set_weights_from_flat(self.global_best_position)

return best_network

class ReinforcementLearningAgent:

"""

Q-Learning agent that coordinates the hybrid system.

Implements goal-oriented behavior and adaptive decision-making.

"""

def __init__(self, state_size: int, action_size: int,

learning_rate: float = 0.1, discount_factor: float = 0.95,

epsilon: float = 1.0, epsilon_decay: float = 0.995):

self.state_size = state_size

self.action_size = action_size

self.learning_rate = learning_rate

self.discount_factor = discount_factor

self.epsilon = epsilon # Exploration rate

self.epsilon_decay = epsilon_decay

self.epsilon_min = 0.01

# Q-table for state-action values

self.q_table = np.zeros((state_size, action_size))

# Experience replay buffer

self.memory = deque(maxlen=2000)

# Performance tracking

self.rewards_history = []

self.actions_history = []

def get_state(self, performance_metrics: Dict[str, float]) -> int:

"""

Convert performance metrics to discrete state.

This is a simplified state representation.

"""

# Discretize performance into states

error_level = min(int(performance_metrics.get('error', 1.0) * 10), self.state_size - 1)

return error_level

def choose_action(self, state: int) -> int:

"""

Choose action using epsilon-greedy policy.

Actions represent different learning strategies:

0: Use individual neural network

1: Use swarm optimization

2: Combine both approaches

"""

if np.random.random() <= self.epsilon:

return np.random.choice(self.action_size) # Explore

else:

return np.argmax(self.q_table[state]) # Exploit

def learn(self, state: int, action: int, reward: float, next_state: int):

"""Update Q-values using Q-learning algorithm"""

current_q = self.q_table[state, action]

max_next_q = np.max(self.q_table[next_state])

# Q-learning update rule

new_q = current_q + self.learning_rate * (

reward + self.discount_factor * max_next_q - current_q

)

self.q_table[state, action] = new_q

# Decay exploration rate

if self.epsilon > self.epsilon_min:

self.epsilon *= self.epsilon_decay

def remember(self, state: int, action: int, reward: float, next_state: int):

"""Store experience in replay buffer"""

self.memory.append((state, action, reward, next_state))

def calculate_reward(self, performance_before: float, performance_after: float) -> float:

"""

Calculate reward based on performance improvement.

Positive reward for improvement, negative for degradation.

"""

improvement = performance_before - performance_after

# Reward function that encourages improvement

if improvement > 0:

reward = min(improvement * 10, 1.0) # Cap positive reward

else:

reward = max(improvement * 10, -1.0) # Cap negative reward

return reward

class HybridIntelligenceSystem:

"""

Main system that integrates neural networks, swarm intelligence, and reinforcement learning.

Represents the complete human-like intelligence architecture.

"""

def __init__(self, network_architecture: List[int], num_swarm_agents: int = 20):

self.network_architecture = network_architecture

self.num_swarm_agents = num_swarm_agents

# Initialize components

self.individual_network = NeuralNetwork(network_architecture)

self.swarm = SwarmIntelligence(num_swarm_agents, network_architecture)

self.rl_agent = ReinforcementLearningAgent(

state_size=10, # 10 discrete error levels

action_size=3 # 3 learning strategies

)

# Performance tracking

self.performance_history = []

self.learning_strategy_history = []

def evaluate_performance(self, X, y, network=None) -> Dict[str, float]:

"""Evaluate current system performance"""

if network is None:

network = self.individual_network

predictions = network.predict(X)

mse = np.mean((predictions - y) ** 2)

mae = np.mean(np.abs(predictions - y))

return {

'mse': mse,

'mae': mae,

'error': mse, # Primary metric for RL

'accuracy': 1.0 / (1.0 + mse) # Inverse relationship

}

def adaptive_learning(self, X, y, episodes: int = 50):

"""

Main learning loop that adaptively chooses learning strategies.

Demonstrates the integration of all three AI paradigms.

"""

print("Starting adaptive learning with hybrid intelligence system...")

print(f"Architecture: {self.network_architecture}")

print(f"Training data shape: {X.shape}, Target shape: {y.shape}")

print("-" * 60)

for episode in range(episodes):

# Get current performance

current_performance = self.evaluate_performance(X, y)

current_state = self.rl_agent.get_state(current_performance)

# Choose learning strategy using RL

action = self.rl_agent.choose_action(current_state)

# Execute chosen strategy

if action == 0: # Individual neural network learning

strategy_name = "Individual NN"

self.individual_network.train(X, y, epochs=50)

elif action == 1: # Swarm optimization

strategy_name = "Swarm Optimization"

best_weights = self.swarm.optimize(X, y, iterations=20)

self.individual_network.set_weights_from_flat(best_weights)

elif action == 2: # Hybrid approach

strategy_name = "Hybrid Approach"

# First use swarm to find good starting point

best_weights = self.swarm.optimize(X, y, iterations=10)

self.individual_network.set_weights_from_flat(best_weights)

# Then fine-tune with individual learning

self.individual_network.train(X, y, epochs=25)

# Evaluate new performance

new_performance = self.evaluate_performance(X, y)

new_state = self.rl_agent.get_state(new_performance)

# Calculate reward and update RL agent

reward = self.rl_agent.calculate_reward(

current_performance['error'],

new_performance['error']

)

self.rl_agent.learn(current_state, action, reward, new_state)

self.rl_agent.remember(current_state, action, reward, new_state)

# Record performance and strategy

self.performance_history.append({

'episode': episode,

'strategy': strategy_name,

'action': action,

'reward': reward,

**new_performance

})

self.learning_strategy_history.append(action)

# Print progress

if episode % 10 == 0:

print(f"Episode {episode:2d}: Strategy={strategy_name:18s} | "

f"MSE={new_performance['mse']:.6f} | "

f"Reward={reward:6.3f} | "

f"Epsilon={self.rl_agent.epsilon:.3f}")

print("-" * 60)

print("Adaptive learning completed!")

# Print final statistics

final_performance = self.performance_history[-1]

print(f"Final MSE: {final_performance['mse']:.6f}")

print(f"Final MAE: {final_performance['mae']:.6f}")

# Strategy usage statistics

strategy_counts = np.bincount(self.learning_strategy_history)

strategy_names = ["Individual NN", "Swarm Optimization", "Hybrid Approach"]

print("\nStrategy Usage:")

for i, (name, count) in enumerate(zip(strategy_names, strategy_counts)):

percentage = (count / len(self.learning_strategy_history)) * 100

print(f" {name}: {count} times ({percentage:.1f}%)")

def visualize_results(self):

"""Create comprehensive visualizations of the learning process"""

if not self.performance_history:

print("No performance history to visualize. Run adaptive_learning first.")

return

fig, axes = plt.subplots(2, 2, figsize=(15, 10))

fig.suptitle('Hybrid Intelligence System Performance Analysis', fontsize=16)

episodes = [p['episode'] for p in self.performance_history]

mse_values = [p['mse'] for p in self.performance_history]

rewards = [p['reward'] for p in self.performance_history]

strategies = [p['action'] for p in self.performance_history]

# Plot 1: MSE over time

axes[0, 0].plot(episodes, mse_values, 'b-', linewidth=2, alpha=0.7)

axes[0, 0].set_title('Mean Squared Error Over Time')

axes[0, 0].set_xlabel('Episode')

axes[0, 0].set_ylabel('MSE')

axes[0, 0].grid(True, alpha=0.3)

# Plot 2: Rewards over time

axes[0, 1].plot(episodes, rewards, 'g-', linewidth=2, alpha=0.7)

axes[0, 1].axhline(y=0, color='r', linestyle='--', alpha=0.5)

axes[0, 1].set_title('Rewards Over Time')

axes[0, 1].set_xlabel('Episode')

axes[0, 1].set_ylabel('Reward')

axes[0, 1].grid(True, alpha=0.3)

# Plot 3: Strategy usage over time

strategy_colors = ['blue', 'orange', 'green']

strategy_names = ['Individual NN', 'Swarm Opt', 'Hybrid']

for i in range(3):

strategy_episodes = [e for e, s in zip(episodes, strategies) if s == i]

strategy_mse = [m for m, s in zip(mse_values, strategies) if s == i]

if strategy_episodes:

axes[1, 0].scatter(strategy_episodes, strategy_mse,

c=strategy_colors[i], label=strategy_names[i],

alpha=0.6, s=30)

axes[1, 0].set_title('MSE by Learning Strategy')

axes[1, 0].set_xlabel('Episode')

axes[1, 0].set_ylabel('MSE')

axes[1, 0].legend()

axes[1, 0].grid(True, alpha=0.3)

# Plot 4: Strategy distribution

strategy_counts = np.bincount(strategies)

axes[1, 1].pie(strategy_counts, labels=strategy_names, colors=strategy_colors,

autopct='%1.1f%%', startangle=90)

axes[1, 1].set_title('Learning Strategy Distribution')

plt.tight_layout()

plt.show()

return fig

# Demonstration: Create and test the hybrid system

print("=" * 80)

print("HYBRID INTELLIGENCE SYSTEM DEMONSTRATION")

print("Combining Neural Networks, Swarm Intelligence, and Reinforcement Learning")

print("=" * 80)

# Generate synthetic dataset for demonstration

print("\n1. Generating synthetic dataset...")

np.random.seed(42)

n_samples = 200

n_features = 4

# Create a non-linear function to approximate

X = np.random.uniform(-2, 2, (n_samples, n_features))

# Complex non-linear target function

y = (np.sin(X[:, 0]) * np.cos(X[:, 1]) +

0.5 * X[:, 2]**2 - 0.3 * X[:, 3]**3 +

0.1 * np.random.randn(n_samples)).reshape(-1, 1)

print(f"Dataset created: {n_samples} samples, {n_features} features")

print(f"Target function: sin(x1)*cos(x2) + 0.5*x3² - 0.3*x4³ + noise")

# Split data

train_size = int(0.8 * n_samples)

X_train, X_test = X[:train_size], X[train_size:]

y_train, y_test = y[:train_size], y[train_size:]

print(f"Training set: {X_train.shape[0]} samples")

print(f"Test set: {X_test.shape[0]} samples")

# Create and train the hybrid system

print("\n2. Initializing Hybrid Intelligence System...")

network_architecture = [n_features, 8, 6, 1] # Input -> Hidden -> Hidden -> Output

hybrid_system = HybridIntelligenceSystem(

network_architecture=network_architecture,

num_swarm_agents=15

)

print(f"Neural Network Architecture: {network_architecture}")

print(f"Swarm Size: {hybrid_system.num_swarm_agents} agents")

print(f"RL Agent: {hybrid_system.rl_agent.state_size} states, {hybrid_system.rl_agent.action_size} actions")

# Train the system

print("\n3. Training with Adaptive Learning...")

hybrid_system.adaptive_learning(X_train, y_train, episodes=30)

# Evaluate final performance

print("\n4. Final Evaluation...")

train_performance = hybrid_system.evaluate_performance(X_train, y_train)

test_performance = hybrid_system.evaluate_performance(X_test, y_test)

print(f"Training Performance:")

print(f" MSE: {train_performance['mse']:.6f}")

print(f" MAE: {train_performance['mae']:.6f}")

print(f" Accuracy: {train_performance['accuracy']:.6f}")

print(f"Test Performance:")

print(f" MSE: {test_performance['mse']:.6f}")

print(f" MAE: {test_performance['mae']:.6f}")

print(f" Accuracy: {test_performance['accuracy']:.6f}")

# Create visualizations

print("\n5. Creating Performance Visualizations...")

fig = hybrid_system.visualize_results()

# Save performance data

performance_data = {

'training_performance': train_performance,

'test_performance': test_performance,

'learning_history': hybrid_system.performance_history,

'system_config': {

'network_architecture': network_architecture,

'num_swarm_agents': hybrid_system.num_swarm_agents,

'dataset_size': n_samples,

'features': n_features

}

# Save to JSON file

with open('hybrid_intelligence_results.json', 'w') as f:

json.dump(performance_data, f, indent=2, default=str)

print("Results saved to 'hybrid_intelligence_results.json'")

print("\n" + "=" * 80)

print("DEMONSTRATION COMPLETED SUCCESSFULLY!")

print("The hybrid system successfully combined:")

print("• Neural Networks for pattern recognition and learning")

print("• Swarm Intelligence for distributed optimization")

print("• Reinforcement Learning for adaptive strategy selection")

print("=" * 80)

Output:

================================================================================

HYBRID INTELLIGENCE SYSTEM DEMONSTRATION

Combining Neural Networks, Swarm Intelligence, and Reinforcement Learning

================================================================================

1. Generating synthetic dataset...

Dataset created: 200 samples, 4 features

Target function: sin(x1)*cos(x2) + 0.5*x3² - 0.3*x4³ + noise

Training set: 160 samples

Test set: 40 samples

2. Initializing Hybrid Intelligence System...

Neural Network Architecture: [4, 8, 6, 1]

Swarm Size: 15 agents

RL Agent: 10 states, 3 actions

3. Training with Adaptive Learning...

Starting adaptive learning with hybrid intelligence system...

Architecture: [4, 8, 6, 1]

Training data shape: (160, 4), Target shape: (160, 1)

------------------------------------------------------------