Evolutionary Optimization Algorithms 🧬

"Evolution is not just a biological process; it is the ultimate optimization algorithm."

📖 About The Project

This repository serves as a practical code companion to Dan Simon's authoritative text, Evolutionary Optimization Algorithms.

The primary goal of this project is to reinforce theoretical concepts through implementation. Instead of relying solely on high-level libraries, algorithms here are implemented from scratch (or with minimal dependencies like numpy) to ensure a deep, mathematical understanding of the underlying mechanics—from stochastic processes to convergence properties.

🎯 Key Objectives

• Deconstruct complex evolutionary heuristics into clean, modular Python code.
• Visualize the optimization process and convergence behaviors.
• Compare theoretical performance (as described in the book) with practical results.

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🗂️ Repository Structure

The algorithms are categorized based on their biological or physical inspiration, following the structure of the book:

• /GeneticAlgorithms: Standard GA implementations (Selection, Crossover, Mutation).
• /EvolutionStrategies: $(\mu, \lambda)$ and $(\mu + \lambda)$ strategies.
• /SwarmIntelligence:
  • Particle Swarm Optimization (PSO)
  • Ant Colony Optimization (ACO)
  • Artificial Bee Colony (ABC)
• /DifferentialEvolution: DE strategies for continuous optimization.
• /PhysicsBased: Simulated Annealing, Harmony Search.

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🚀 Getting Started

Prerequisites

The implementations rely primarily on scientific computing libraries for matrix operations and visualization.

pip install numpy matplotlib

Usage Example (OneMax Problem)

To run a basic Genetic Algorithm on the OneMax problem:

Python
from EvolutionaryOptimizationAlgorithms import firstGA

# Initialize the optimizer
optimizer = SimpleGA(pop_size=50, gene_length=20, mutation_rate=0.01)

# Run evolution
best_solution, history = optimizer.evolve(generations=100)

print(f"Global Optimum Found: {best_solution}")

📈 Roadmap & Progress

[x] Chapter 1-3: Introduction & Optimization Basics

[ ] Chapter 4: Genetic Algorithms (Implementation in progress)

[ ] Chapter 8: Evolution Strategies

[ ] Chapter 11: Differential Evolution

[ ] Chapter 12: Particle Swarm Optimization

[ ] Chapter 13: Ant Colony Optimization

📚 Reference This work is based on the concepts presented in:

Simon, D. (2013). Evolutionary Optimization Algorithms. John Wiley & Sons.

🛡️ License Distributed under the MIT License. See LICENSE for more information.

Created by archiristo — A tribute to the beauty of stochastic optimization.