genealgolib 0.1.0

A high-performance genetic algorithm library implemented in Rust with Python bindings

GeneAlgoLib

A blazingly fast genetic algorithm library implemented in Rust with Python bindings via PyO3.

Features

• High Performance: Rust implementation provides significant speedup over pure Python/NumPy implementations
• Simple API: Easy-to-use Python interface
• Tournament Selection: Efficient selection mechanism with configurable tournament size
• Elitism: Preserves best individuals across generations
• Configurable: Adjustable population size, mutation rate, and gene count

Installation

pip install genealgolib

Quick Start

import numpy as np
from fast_ga import GeneticAlgorithm

# Initialize GA
ga = GeneticAlgorithm(
    pop_size=1000,      # Population size
    num_genes=10,       # Number of genes per individual
    mutation_rate=0.05  # Mutation probability
)

# Evolution loop
for generation in range(100):
    # Get current population
    population = ga.get_population()
    
    # Calculate fitness scores (example: sum to 10.0)
    sums = np.sum(population, axis=1)
    fitness_scores = 1.0 / (np.abs(10.0 - sums) + 1e-6)
    
    # Evolve to next generation
    ga.evolve(fitness_scores)

# Get final population
final_pop = ga.get_population()

Performance

GeneAlgoLib significantly outperforms highly optimized NumPy implementations:

• Average Speedup: 2-5x faster than vectorized NumPy
• Scales Well: Better performance with larger populations
• Memory Efficient: Optimized memory usage in Rust

API Reference

GeneticAlgorithm(pop_size, num_genes, mutation_rate)

Initialize a genetic algorithm instance.

Parameters:

• pop_size (int): Number of individuals in the population
• num_genes (int): Number of genes per individual
• mutation_rate (float): Probability of mutation (0.0 to 1.0)

get_population()

Returns the current population as a NumPy array of shape (pop_size, num_genes).

evolve(fitness_scores)

Evolve the population to the next generation.

Parameters:

• fitness_scores (np.ndarray): 1D array of fitness scores for each individual

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.