Simulation Hypothesis
Machine Learning
A probabilistic optimization technique inspired by the annealing process in metallurgy that gradually decreases the probability of accepting worse solutions.
Detailed Explanation
Simulated Annealing is a probabilistic optimization algorithm inspired by the metallurgical process of annealing. It explores the solution space by allowing occasional acceptance of worse solutions early on, reducing this tendency over time. This approach helps prevent local minima, enabling the algorithm to find near-global optimal solutions for complex problems in machine learning and combinatorial optimization.
Use Cases
- •Optimizing complex scheduling problems by gradually exploring and refining solutions to avoid local minima.
Related Terms
Other terms in the Machine Learning category