Informed Search Algorithms
Informed Search Algorithms Using Python Libraries
Informed Search Algorithms
Informed Search Algorithms Using Python Libraries
This post demonstrates informed search algorithms using existing Python libraries and frameworks, showing how to leverage pre-built implementations for real-world applications.
📚 Libraries & Frameworks Used:
- NetworkX: Graph algorithms and A* implementation
- SciPy: Optimization algorithms (Simulated Annealing, etc.)
- scikit-opt: Genetic Algorithm and other metaheuristics
- OR-Tools: Google’s optimization tools (Tabu Search equivalent)
📓 Interactive Notebook
For the complete interactive implementation with code examples, visualizations, and detailed explanations, please view the html version of the Jupyter notebook:
📊 View the Interactive Notebook
To download the notebook, click the link below: 💾 Download the Jupyter Notebook
Note: The notebook contains executable Python code demonstrating each algorithm with real examples and performance comparisons.
Key Algorithms Covered
- A* Algorithm - Using NetworkX for pathfinding
- Simulated Annealing - Using SciPy for optimization problems
- Genetic Algorithm - Using scikit-opt for evolutionary computation
- Local Search Variants - Using OR-Tools for constraint satisfaction
Each algorithm is demonstrated with practical examples and performance analysis.
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