1. 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

2. 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.

3. 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.

4. 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.

5. Uncross Problem