🧬 Bio-Inspired Optimization Techniques

This repository contains projects and experiments based on bio-inspired optimization techniques, with applications in function optimization, pattern recognition, probability simulation, and image contrast enhancement.

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📂 Repository Structure

1. Genetic Algorithm Function Optimization

• Files:
  • GA_Function.ipynb: Jupyter Notebook for the implementation.
  • GA_Function_Optimization.pdf: Detailed report on optimization results.
• Description:
  • Optimized benchmark functions such as Sphere, Griewank, Ackley, Rosenbrock, Zakharov, and Rastrigin using Genetic Algorithms (GA).
  • Key operations:
    • Selection: Roulette Wheel.
    • Crossover: Genetic recombination.
    • Mutation: Variational changes in the population.

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2. Genetic Algorithm Pattern Recognition

• Files:
  • GA.ipynb: Implementation of the pattern recognition algorithm.
  • 3122225002013_GA_Report.pdf: Report documenting the process.
  • Sample Images: one1.jpg, zero1.jpg.
• Description:
  • Recognized sample digits by applying Genetic Algorithms to a population of random images.

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3. PMF Simulation of Dice Rolling

• Description:
  • Verified the provided Probability Mass Function (PMF).
  • Analyzed entropy to understand the randomness of outcomes.

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4. Stochastic Resonance Image Contrast Enhancement

• Files:
  • SR_img_prc.ipynb: Jupyter Notebook for the implementation.
  • SR_Contrast_Enhancement.pdf: Report documenting the process.
  • Sample Images: degraded.jpg, Mona_Lisa_GS2.jpg, sam.jpg.
• Description:
  • Enhanced the contrast of degraded sample images using the Stochastic Resonance (SR) technique. By Adding suitable amount of noise, the performance of the system in enhanced.

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🤝 Contributions We welcome contributions to improve and expand this repository.

• Fork the repository.
• Implement your changes and test thoroughly.
• Submit a pull request with a clear explanation of your updates.