1. Local Search Pat Riddle 2012 Semester 2 Patricia J Riddle Adapted from slides by Stuart Russell, http://aima.cs.berkeley.edu/instructors.html

2. Local search algorithms In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution State space = set of "complete" configurations Find optimal configuration, e.g., TSP Find configuration satisfying constraints, e.g., timetable, n-queens In such cases, we can use local search algorithms (iterative improvement algorithms) keep a single "current" state, try to improve it Constant space, suitable for online as well as offline search 2

3. Example: n-queens Put n queens on an n × n board with no two queens on the same row, column, or diagonal Move a queen to reduce number of conflicts Almost always solves n-queens problems almost instantaneously for very large n, e.g., n=1million 3

4. Hill-climbing search: 8-queens problem h = number of pairs of queens that are attacking each other, either directly or indirectly h = 17 for the above state 4

5. Hill-climbing search: 8-queens problem A local minimum with h = 1 5

6. Example: Travelling Salesperson Problem Start with any complete tour, perform pairwise exchanges Variants of this approach get within 1% of optimal very quickly with thousands of cities 6

7. Hill-climbing search "Like climbing Everest in thick fog with amnesia" 7

8. Hill-climbing search Problem: depending on initial state, can get stuck in local maxima 8

9. Hill-climbing with 8-queens Gets stuck 86% of the time Solves only 14% of problem instances – 4 steps on average when it succeeds – 3 steps on average when it gets stuck 9

10. Hill climbing fixes 10

11. Sideways Moves escape from shoulders loop on at maxima Limit number of consecutive sideways steps 100 consecutive sideways moves in 8-queens Now Solves 94% of problem instances (up from 14%) Average 21 moves when succeeds Average 64 moves when it fails 11

12. Stochastic Hill Climbing Choose randomly among the uphill moves Or probability of selection based on steepness of move Converges more slowly, but can find better solutions 12

13. First Choice Hill-climbing Implementation of stochastic hill-climbing Generating successors randomly until it finds one which is uphill – Very good when a state has many successors – Don’t waste time generating them all 13

14. Random-restart hill climbing series of hill-climbing searches (from randomly generated initial states) overcomes local maxima trivially complete 14

15. 8-queens with Random Restarts p is probability of success Number of restarts is 1/p 8 queens, no sideways, p=0.14, so ~7 restarts 8 queens, sideways, p=0.94, so ~1.06 restarts Number of steps (1 x cost of success) + ((1-p)/p) x cost of failure) 8 queens, no sideways, (1 x 4) + ((.86/.14) x 3) ~ 22 steps 8 queens, sideways, (1x21) + ((0.06/0.94) x 64) ~ 25 steps For 3 million queens, random restart finds solutions in under a minute 15

16. Hill Climbing Summary The success of hill climbing depends very much on the shape of the state-space landscape. 16

17. Simulated annealing search Idea: escape local maxima by allowing some "bad" moves but gradually decrease their size and frequency 17

18. Properties of simulated annealing search One can prove: If T decreases slowly enough, then simulated annealing search will find a global optimum with probability approaching 1 Intuition: Shake hard enough to dislodge from local minima but not hard enough to dislodge from global minima Widely used in VLSI layout, airline scheduling, etc 18

19. Local beam search Keep track of k states rather than just one, choose top k of all their successors Start with k randomly generated states At each iteration, all the successors of all k states are generated If any one is a goal state, stop; else select the k best successors from the complete list and repeat. 19

20. Same as k random restarts? NO In a local beam search, useful information is passed among the parallel search threads. But can quickly become concentrated in a small region 20

21. Stochastic Beam Search Choose k successors randomly, biased towards good ones Observe the close analogy to natural selection! 21

22. Genetic algorithms 1 = stochastic local beam search + generate successors from pairs of states 22

23. Genetic algorithms 2 A successor state is generated by combining two parent states Start with k randomly generated states (population) A state is represented as a string over a finite alphabet (often a string of 0s and 1s) Evaluation function (fitness function). Higher values for better states. Produce the next generation of states by selection, crossover, and mutation 23

24. Genetic Algorithms 3 GAs frequently use states encoded as strings (GPs use programs - trees) Crossover helps iff substrings are meaningful components 24