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.

Uncross Problem