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🧭 Hybrid Algorithms for Hyperparameter Optimization

A 3D loss-landscape simulation where search agents explore randomly, then switch to local gradient refinement — showing how hybrid multi-stage algorithms combine global and local hyperparameter search.

AI & Machine Learning3DAdvanced60 FPS

A field of search agents hunts for the lowest point of a 3D loss landscape, first exploring broadly at random and then descending local gradients to refine their best finds — the two-stage pattern behind real hybrid hyperparameter tuning pipelines.

🔬 What It Demonstrates

Pure random search covers the space but converges slowly; pure gradient descent converges fast but gets trapped in whichever basin it starts near. Hybrid search runs exploration first to find promising basins, then refinement to polish them — usually reaching a better minimum, faster, than either method alone.

🎮 How to Use

Pick an algorithm mode to compare strategies, adjust how many agents search in parallel and how rugged (multi-modal) the loss surface is, and tune how long the hybrid strategy explores before switching to local refinement. Watch the best-loss stat drop as agents converge toward the gold ring marking the true global minimum.

💡 Did You Know?

Real AutoML systems like Bayesian Optimization with Hyperband (BOHB) and population-based training use this exact combination — cheap global exploration to avoid missing good hyperparameter regions, followed by expensive local refinement only where it is likely to pay off.

⚙ Under the hood

machine learninghyperparameter optimizationalgorithmssearch algorithmssimulationoptimizationThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install