Neuroevolution

The neuroevolution module provides evolutionary algorithms for optimizing neural network topologies and weights, including NEAT, HyperNEAT, and CMA-ES. These methods are particularly effective for reinforcement learning and optimization problems where gradient-based methods struggle.

Overview

Neuroevolution evolves neural networks through:

Topology Evolution: Discovering optimal network architectures
Weight Optimization: Finding effective connection weights
Feature Learning: Evolving representations without supervision

NEAT (NeuroEvolution of Augmenting Topologies)

NEAT evolves both the topology and weights of neural networks simultaneously, starting from minimal structures and complexifying over generations.

Basic Usage

from neurenix.neuroevolution import NEAT, NEATConfig

# Configure NEAT
config = NEATConfig()
config.population_size = 150
config.compatibility_threshold = 3.0
config.species_elitism = 0.2
config.add_node_mutation_rate = 0.03
config.add_connection_mutation_rate = 0.05

# Initialize NEAT
neat = NEAT(config)
neat.initialize(
    population_size=150,
    num_inputs=4,
    num_outputs=1
)

# Define fitness function
def evaluate_genome(genome):
    network = neat.get_network(genome)
    fitness = 0.0
    
    # Evaluate on XOR problem
    test_cases = [
        ([0, 0], 0),
        ([0, 1], 1),
        ([1, 0], 1),
        ([1, 1], 0)
    ]
    
    for inputs, expected in test_cases:
        output = network(nx.tensor([inputs]))
        error = (output.item() - expected) ** 2
        fitness -= error
    
    return fitness

# Evolve for multiple generations
for generation in range(100):
    neat.evolve(evaluate_genome)
    
    best = neat.get_best_genome()
    print(f"Generation {generation}: Best Fitness = {best.fitness:.4f}")

# Get the best network
best_genome = neat.get_best_genome()
best_network = neat.get_network(best_genome)

NEAT Configuration

config = NEATConfig()

# Species parameters
config.compatibility_threshold = 3.0
config.excess_coefficient = 1.0
config.weight_coefficient = 0.4
config.species_elitism = 0.2
config.species_stagnation_threshold = 15

# Mutation rates
config.weight_mutation_rate = 0.8
config.weight_perturb_prob = 0.9
config.add_node_mutation_rate = 0.03
config.add_connection_mutation_rate = 0.05

# Reproduction
config.asexual_reproduction_rate = 0.25

# Activation functions
config.activation_functions = ['sigmoid', 'tanh', 'relu']

Working with Genomes

from neurenix.neuroevolution import NEATGenome, NodeGene, ConnectionGene, NodeType

# Create a genome manually
genome = NEATGenome()

# Add input nodes
for i in range(2):
    node = NodeGene(i, NodeType.INPUT)
    genome.add_node(node)

# Add output node
output_node = NodeGene(2, NodeType.OUTPUT)
genome.add_node(output_node)

# Add connections
conn1 = ConnectionGene(0, 2, weight=0.5, innovation=0)
conn2 = ConnectionGene(1, 2, weight=-0.3, innovation=1)
genome.add_connection(conn1)
genome.add_connection(conn2)

# Mutate genome
genome.mutate_weight()
genome.mutate_add_node(innovation_history)

HyperNEAT

HyperNEAT extends NEAT to evolve large-scale networks by using a CPPN (Compositional Pattern Producing Network) to generate connection weights based on geometric patterns.

from neurenix.neuroevolution import HyperNEAT, Substrate, NEATConfig

# Define substrate (network geometry)
input_coords = [
    [-1.0, -1.0],
    [-1.0, 1.0],
    [1.0, -1.0],
    [1.0, 1.0]
]

hidden_coords = [
    [-0.5, 0.0],
    [0.0, 0.0],
    [0.5, 0.0]
]

output_coords = [
    [0.0, 1.0]
]

substrate = Substrate(
    input_coords=input_coords,
    hidden_coords=hidden_coords,
    output_coords=output_coords
)

# Initialize HyperNEAT
config = NEATConfig()
hyperneat = HyperNEAT(
    substrate=substrate,
    neat_config=config,
    weight_threshold=0.2,
    activation='tanh'
)

# Initialize population of CPPNs
hyperneat.initialize(population_size=100)

# Define fitness function
def evaluate_network(network):
    fitness = 0.0
    # Evaluate network performance
    for inputs, target in test_data:
        output = network(nx.tensor([inputs]))
        fitness += compute_fitness(output, target)
    return fitness

# Evolve
hyperneat.evolve(
    fitness_function=evaluate_network,
    generations=50
)

# Get best network
best_network = hyperneat.get_best_network()

Custom Substrate

# 2D grid substrate for image processing
width, height = 28, 28
input_coords = [
    [x / width, y / height] 
    for x in range(width) 
    for y in range(height)
]

output_coords = [[0.5, 0.5 + i/10] for i in range(10)]  # 10 classes

substrate = Substrate(
    input_coords=input_coords,
    output_coords=output_coords
)

CMA-ES (Covariance Matrix Adaptation Evolution Strategy)

CMA-ES is a state-of-the-art evolutionary algorithm for continuous optimization.

from neurenix.neuroevolution import CMAES, CMAESConfig
import numpy as np

# Configure CMA-ES
config = CMAESConfig(
    sigma=0.5,              # Initial step size
    population_size=None,   # Auto-determined
    max_iterations=1000
)

# Initialize CMA-ES
dimension = 10
initial_mean = np.zeros(dimension)
cmaes = CMAES(dimension, initial_mean, config)

# Define objective function (minimize)
def sphere_function(x):
    return np.sum(x ** 2)

# Optimize
best_solution, best_fitness = cmaes.optimize(
    objective_function=sphere_function,
    iterations=100
)

print(f"Best solution: {best_solution}")
print(f"Best fitness: {best_fitness}")

Optimizing Neural Networks with CMA-ES

from neurenix.neuroevolution import CMAESModel
from neurenix.nn import Sequential, Linear
import neurenix as nx

# Define model
model = Sequential(
    Linear(10, 32),
    nx.nn.ReLU(),
    Linear(32, 1)
)

# Create CMA-ES optimizer
cmaes_model = CMAESModel(model, config)

# Training data
X_train = nx.randn(1000, 10)
y_train = nx.randn(1000, 1)

# Loss function
def mse_loss(pred, target):
    return ((pred - target) ** 2).mean().item()

# Optimize
best_loss = cmaes_model.fit(
    X_train,
    y_train,
    loss_fn=mse_loss,
    iterations=100,
    verbose=True
)

print(f"Best loss: {best_loss}")

CMA-ES Configuration

config = CMAESConfig(
    sigma=0.5,                    # Initial step size
    population_size=None,         # Auto: 4 + floor(3*log(N))
    parent_number=None,           # Auto: population_size // 2
    weights_option='default',     # 'default', 'equal', 'linear'
    active=True,                  # Use active CMA
    diagonal_iterations=0,        # Diagonal covariance iterations
    tolx=1e-12,                   # Tolerance for x changes
    tolfun=1e-12,                 # Tolerance for function changes
    tolstagnation=100,            # Stagnation tolerance
    max_iterations=1000
)

Evolution Strategies

General evolution strategies framework.

from neurenix.neuroevolution import EvolutionStrategy, ESConfig

config = ESConfig(
    population_size=50,
    elite_size=10,
    mutation_rate=0.1,
    mutation_strength=0.01
)

es = EvolutionStrategy(config)

# Initialize population
es.initialize(dimension=100)

# Evolution loop
for generation in range(100):
    # Evaluate population
    fitnesses = [evaluate(individual) for individual in es.population]
    
    # Update
    es.update(fitnesses)
    
    print(f"Gen {generation}: Best = {es.best_fitness:.4f}")

Genetic Algorithms

from neurenix.neuroevolution import (
    GeneticAlgorithm,
    Population,
    Individual,
    Crossover,
    Mutation,
    Selection
)

# Create genetic algorithm
ga = GeneticAlgorithm(
    population_size=100,
    chromosome_length=50,
    mutation_rate=0.01,
    crossover_rate=0.7
)

# Define fitness function
def fitness_function(individual):
    return sum(individual.genes)

# Evolve
for generation in range(100):
    ga.evaluate(fitness_function)
    ga.selection(method='tournament', tournament_size=3)
    ga.crossover(method='single_point')
    ga.mutation(method='bit_flip')
    
    best = ga.get_best_individual()
    print(f"Gen {generation}: Fitness = {best.fitness}")

Example: Cart-Pole with NEAT

import neurenix as nx
from neurenix.neuroevolution import NEAT, NEATConfig
import gym

def evaluate_cartpole(genome, neat):
    """Evaluate genome on CartPole environment"""
    env = gym.make('CartPole-v1')
    network = neat.get_network(genome)
    
    total_reward = 0
    num_episodes = 5
    
    for episode in range(num_episodes):
        observation = env.reset()
        done = False
        episode_reward = 0
        
        while not done:
            # Get action from network
            output = network(nx.tensor([observation]))
            action = 1 if output.item() > 0.5 else 0
            
            # Step environment
            observation, reward, done, _ = env.step(action)
            episode_reward += reward
            
            if done:
                break
        
        total_reward += episode_reward
    
    env.close()
    return total_reward / num_episodes

# Setup NEAT
config = NEATConfig()
config.population_size = 150
neat = NEAT(config)
neat.initialize(population_size=150, num_inputs=4, num_outputs=1)

# Evolve
for generation in range(50):
    neat.evolve(lambda g: evaluate_cartpole(g, neat))
    
    best = neat.get_best_genome()
    print(f"Generation {generation}: Fitness = {best.fitness:.2f}")
    
    # Stop if solved
    if best.fitness >= 195:
        print("Environment solved!")
        break

Example: Function Optimization with CMA-ES

import numpy as np
from neurenix.neuroevolution import CMAES, CMAESConfig

# Rastrigin function (challenging multi-modal optimization)
def rastrigin(x):
    A = 10
    n = len(x)
    return A * n + np.sum(x**2 - A * np.cos(2 * np.pi * x))

# Initialize CMA-ES
config = CMAESConfig(
    sigma=0.5,
    max_iterations=300
)

dimension = 10
initial_mean = np.random.randn(dimension) * 2
cmaes = CMAES(dimension, initial_mean, config)

# Optimize
for iteration in range(300):
    # Ask for new candidate solutions
    solutions = cmaes.ask()
    
    # Evaluate
    fitnesses = [rastrigin(x) for x in solutions]
    
    # Tell results back to optimizer
    cmaes.tell(solutions, fitnesses)
    
    if iteration % 10 == 0:
        best_sol, best_fit = cmaes.get_best()
        print(f"Iteration {iteration}: Best = {best_fit:.6f}")

best_solution, best_fitness = cmaes.get_best()
print(f"\nOptimization complete!")
print(f"Best solution: {best_solution}")
print(f"Best fitness: {best_fitness}")

Best Practices

Population Size: Larger populations explore more thoroughly but evolve slower
Mutation Rates: Start with low rates and adjust based on performance
Fitness Evaluation: Use multiple trials to reduce noise
Speciation: NEAT’s speciation protects innovation and prevents premature convergence
Termination: Monitor both fitness improvement and population diversity

When to Use Neuroevolution

Use NEAT when:

Network topology is unknown
Starting from minimal structures
Sparse connectivity is desired

Use HyperNEAT when:

Large-scale networks are needed
Geometric patterns are important
Regular structure is beneficial

Use CMA-ES when:

Optimizing continuous parameters
Gradient information is unavailable
Robustness to noise is needed

References

Stanley & Miikkulainen (2002) - “Evolving Neural Networks through Augmenting Topologies”
Stanley et al. (2009) - “A Hypercube-Based Indirect Encoding for Evolving Large-Scale Neural Networks”
Hansen & Ostermeier (2001) - “Completely Derandomized Self-Adaptation in Evolution Strategies”

Get Started

Core Concepts

AI Agents

Reinforcement Learning

Advanced Features

Specialized Modules

Hardware Support

Deployment

Neuroevolution

Neuroevolution

Overview

NEAT (NeuroEvolution of Augmenting Topologies)

Basic Usage

NEAT Configuration

Working with Genomes

HyperNEAT

Custom Substrate

CMA-ES (Covariance Matrix Adaptation Evolution Strategy)

Optimizing Neural Networks with CMA-ES

CMA-ES Configuration

Evolution Strategies

Genetic Algorithms

Example: Cart-Pole with NEAT

Example: Function Optimization with CMA-ES

Best Practices

When to Use Neuroevolution

References

See Also

Build docs developers (and LLMs) love

Get Started

Core Concepts

AI Agents

Reinforcement Learning

Advanced Features

Specialized Modules

Hardware Support

Deployment

Documentation Index

​Neuroevolution

​Overview

​NEAT (NeuroEvolution of Augmenting Topologies)

​Basic Usage

​NEAT Configuration

​Working with Genomes

​HyperNEAT

​Custom Substrate

​CMA-ES (Covariance Matrix Adaptation Evolution Strategy)

​Optimizing Neural Networks with CMA-ES

​CMA-ES Configuration

​Evolution Strategies

​Genetic Algorithms

​Example: Cart-Pole with NEAT

​Example: Function Optimization with CMA-ES

​Best Practices

​When to Use Neuroevolution

​References

​See Also

Build docs developers (and LLMs) love

Neuroevolution

Overview

NEAT (NeuroEvolution of Augmenting Topologies)

Basic Usage

NEAT Configuration

Working with Genomes

HyperNEAT

Custom Substrate

CMA-ES (Covariance Matrix Adaptation Evolution Strategy)

Optimizing Neural Networks with CMA-ES

CMA-ES Configuration

Evolution Strategies

Genetic Algorithms

Example: Cart-Pole with NEAT

Example: Function Optimization with CMA-ES

Best Practices

When to Use Neuroevolution

References

See Also