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🧬 Evolutionary Algorithms for Hyperparameter Optimization

Watch a population of candidate hyperparameter configurations evolve across a 3D loss landscape using a genetic algorithm or particle swarm optimizer.

AI & Machine Learning3DAdvanced60 FPS

A population of candidate hyperparameter configurations searches a 3D validation-loss landscape generation by generation, using either a genetic algorithm or particle swarm optimization.

🔬 What It Demonstrates

The purple-green-red surface encodes validation loss over two hyperparameter axes. Each sphere is a candidate configuration; every generation the population is selected, recombined and mutated (or accelerated toward personal/global bests) so it drifts toward the landscape's minima.

🎮 How to Use

Choose Genetic Algorithm or Particle Swarm, pick a smooth or rugged landscape, and tune population size, mutation/exploration and selection pressure. Step one generation at a time or let it evolve automatically, and watch the best-loss marker track the global minimum.

💡 Did You Know?

Population-based training and CMA-ES are used in production ML pipelines precisely because hyperparameter loss surfaces are usually non-differentiable and riddled with local minima — exactly what the rugged landscape here simulates.

⚙ Under the hood

Watch a population of candidate hyperparameter configurations evolve across a 3D loss landscape using a genetic algorithm or particle swarm optimizer.

machine learningoptimizationalgorithmsgenetic algorithmsevolutionary computationhyperparametersThree.js

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