🔬 Evolutionary Algorithms: Differential Evolution vs. Genetic Algorithm

This project implements and compares two powerful evolutionary optimization algorithms: Differential Evolution (DE) and Genetic Algorithm (GA). Both algorithms are applied to a suite of benchmark functions to explore their performance in solving complex, non-linear optimization problems.

📌 Benchmark Functions

• 🌀 Ackley

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• 🏔️ Bukin

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• ✖️ Cross-in-Tray

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• 🌊 Drop-Wave

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• 🍳 EggHolder

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Each function is visualized in both 2D and 3D plots, highlighting the best solution found by each algorithm on the function’s surface.

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✅ Features

• 🔁 Differential Evolution (DE) implementation
• 🧬 Genetic Algorithm (GA) with:
  • Crossover
  • Mutation
  • Roulette Wheel Selection
• 🧠 Optimization over 5 well-known benchmark functions
• 📈 2D & 3D function visualization using Matplotlib
• 🐍 Fully implemented in Python with NumPy

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📊 Output

• Prints best fitness per generation for each algorithm
• Displays 2D contour plots and 3D surface plots
• Shows best-found solution points for DE and GA on each function

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📎 License

This project is open-source under the MIT License.

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🔗 References

Benchmark function definitions were adapted from the collection maintained by SigOpt and Surjanovic & Bingham (Simon Fraser University):
🔗 https://www.sfu.ca/~ssurjano/

🛠️ Getting Started

Clone the repo and run:

EA.py