Can you explain how hill climbing works in optimization?
Sure! Hill climbing is an optimization algorithm that starts with an arbitrary solution and iteratively makes small changes to improve it.
What kind of problems can hill climbing solve?
Hill climbing is often used for problems like function optimization, scheduling, and pathfinding, where a solution can be improved incrementally.
Are there any limitations to hill climbing?
Yes, hill climbing can get stuck in local maxima, meaning it might not find the best overall solution. It also doesn't guarantee optimality.
How does it compare to other optimization methods?
Compared to methods like simulated annealing or genetic algorithms, hill climbing is simpler but less robust in escaping local optima.
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