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Hill-climbing Search. Goal: Optimizing an objective function. Can be applied to “goal” predicate type of problems. BSAT with objective function number of clauses satisfied. Intuition: Always move to a better state. Some Hill-Climbing Algo’s.
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Hill-climbing Search • Goal: Optimizing an objective function. • Can be applied to “goal” predicate type of problems. • BSAT with objective function number of clauses satisfied. • Intuition: Always move to a better state
Some Hill-Climbing Algo’s • Start State = empty state or random state or special state • Until (no improvement) • Steepest Ascent: find best successor • OR (greedy): select first improving successor • Go to that successor • Repeat the above process some number of times (Restarts). • Can be done with partial solutions or full solutions.
Local (Iterative) Improving • Initial state = full candidate solution • Random or “nearest neighbor selection” • Greedy hill-climbing: • if up, do it • if flat, probabilistically decide to accept move • Not necessary for homework • Otherwise need to limit number of flat moves • if down, don’t do it • We are gradually expanding the possible moves.
TSP Notes • Only use “up” moves • It is not necessary to compute the entire tour lengths after an operator applies. Instead compute change in length- most of the path length stays the same. • You keep applying the operators until no improvement occurs.
