fuzzy_ga

⚡️ Fuzzy Logic + Genetic Algorithm
A prototype of a decision optimization system based on fuzzy logic and genetic algorithms.

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🔍 Overview

The project implements a hybrid approach:

• 🌀 Fuzzy logic is used to model rules and handle uncertainty.
• 🧬 Genetic algorithms are applied to search for optimal solutions.

This combination makes it possible to solve problems where classical optimization struggles due to a large number of factors and uncertain conditions.

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✨ Potential Applications

• ⚙️ Parameter optimization (e.g., production process tuning).
• 📊 Decision support systems.
• 🤖 Intelligent controllers and expert systems.

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🚀 Quick Start

# Clone the project
git clone https://github.com/chybatronik/fuzzy_ga.git
cd fuzzy_ga

# Run demo (optimization example)
go run main.go

Example output:

Generation 1: Best fitness = 0.42
Generation 2: Best fitness = 0.67
Generation 3: Best fitness = 0.91
...
Optimal solution found: [X=12.4, Y=3.7]

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🛠 Technologies

• Go (core implementation)
• Fuzzy logic algorithms
• Genetic algorithm (GA)

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📊 Algorithm Architecture

flowchart TD
    A[Initialize population] --> B[Evaluate fitness using fuzzy rules]
    B --> C[Select best solutions]
    C --> D[Crossover and mutations]
    D --> B
    C --> E[Optimal solution]

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📖 Learn More

• Fuzzy logic
• Genetic algorithm (GA)

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👤 Author

Developed as a research project while studying Go and artificial intelligence algorithms.