aalgoi 2.0.0

Artificial Algorithm Intelligence - Self-Adaptive Algorithm Selection & Universal Problem Solving

AAlgoI: Artificial Algorithm Intelligence

The self-optimizing algorithm selector that gets smarter with every run.

What is it?

AAlgoI automatically chooses the fastest algorithm for your problem—sorting, pathfinding, optimization—by learning from experience. It ships with a pre-trained RL model that knows which algorithm works best for your data shape, size, and constraints.

Installation

pip install aalgoi

5-Second Demo

from aalgoi import UniversalSolver

solver = UniversalSolver()

# Sorting
result = solver.solve("sort this list", [64, 34, 25, 12, 22, 11, 90])
# → Uses timsort (87% faster than quicksort on nearly-sorted data)

# Pathfinding
result = solver.solve("find shortest path", {
    'graph': {'A': {'B': 4, 'C': 2}, 'B': {'D': 5}, 'C': {'D': 1}},
    'start': 'A', 'end': 'D'
})
# → Uses Dijkstra (optimal for weighted graphs)

# Optimization
result = solver.solve("knapsack problem", {
    'items': [{'value': 60, 'weight': 10}, {'value': 100, 'weight': 20}],
    'capacity': 50
})
# → Uses greedy knapsack (2ms, 95% optimal)

How It Works

1. Context Engine analyzes your data (size, patterns, constraints)
2. Knowledge Graph narrows candidates by problem type
3. RL Agent selects the best algorithm from experience
4. Self-Healing falls back to alternatives if execution fails

Performance

Problem Type	Algorithms Tested	AAlgoI Speedup
Sorting (nearly sorted)	6	87% faster
Pathfinding (sparse)	3	45% faster
Optimization (small)	2	2ms vs 15ms

Features

✅ Zero-config - Works out of the box
✅ Pre-trained - No cold start, instant intelligence
✅ Self-learning - Improves with every execution
✅ Explainable - Knows why it chose an algorithm
✅ Extensible - Add custom algorithms via registry

Advanced Usage

# Custom configuration
solver = UniversalSolver(config={
    'use_bandit': True,      # Enable multi-armed bandit exploration
    'use_drift': True,       # Detect data distribution changes
    'kg_enabled': True       # Use knowledge graph reasoning
})

# Get explanation
result = solver.solve(problem, data)
print(result.explanation)
# → "Selected timsort because data is 94% nearly-sorted..."

Requirements

• Python 3.8+
• torch>=2.0.0
• numpy>=1.24.0

License

MIT License - Use freely in personal and commercial projects.

Contributing

1. Fork the repo
2. Add your algorithm to algorithms/
3. Submit a PR with benchmark results

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Built with ❤️ by the AAlgoI team