Cross-entropy method
term_id: cross_entropy_method
Category: basic_concepts
Definition
The Cross-Entropy Method (CEM) is a powerful general-purpose optimization algorithm used for both discrete and continuous problems. It works by maintaining a probability distribution over the search space, sampling candidate solutions, and updating the distribution based on the top-performing samples. This iterative process narrows down the search space towards optimal solutions, making it particularly effective for complex, non-differentiable, or high-dimensional optimization tasks where gradient-based methods fail.
Summary
A randomized optimization technique that uses Monte Carlo simulation to iteratively improve estimates of rare-event probabilities.
Key Concepts
- Monte Carlo Simulation
- Iterative Refinement
- Probability Distribution Update
- Elite Samples
Use Cases
- Robotics path planning
- Game AI strategy optimization
- Rare event estimation in risk analysis