CS 3243 – Algorithms review

Chapters 2-4 and 6

Note: you will want to print this with notes.

Greedy best-first vs. A* search

Greedy best first expands the node that gives the least estimated cost to the goal: f(n) = h(n). It ignores the step cost entirely.

A* combines uniform cost search and greedy best first search f(n) = g(n) + h(n).

Local Beam Search

Idea: instead of one state, keep track of many.

Begins at k random states

Generates all successors, keeps k best for next step.

Genetic Algorithms

(pg 119, figure 4.17)

Consists three parts:

a pool of states (also called individuals)

genetic crossbreeding of states according to some fitness

mutation of population

Minimax algorithm

The α-β algorithm


	Chapters 2-4 and 6
	Note: you will want to print this with notes.


	Greedy best first expands the node that gives the least estimated cost to the goal: f(n) = h(n). It ignores the step cost entirely.
	A* combines uniform cost search and greedy best first search f(n) = g(n) + h(n).


	Idea: instead of one state, keep track of many.
	Begins at k random states
	Generates all successors, keeps k best for next step.


	(pg 119, figure 4.17)
	Consists three parts:
		a pool of states (also called individuals)
		genetic crossbreeding of states according to some fitness
		mutation of population