 Stochastic hill climbing

Stochastic hill climbing is a variation of the traditional hill climbing optimization algorithm that introduces randomness into the process of selecting the next move in the search space. While standard hill climbing evaluates neighboring solutions sequentially and chooses the best among them, stochastic hill climbing selects its next move based on a probability distribution, allowing it to potentially escape local optima and explore the search space more broadly. Here’s how it generally works: 1. **Current Solution**: Start with an initial solution (or state).