Traversal

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A graph-search technique that explores a tree or graph by visiting nodes according to a specified rule.

Graph traversal explores a tree or graph by visiting nodes according to a specific, well-defined rule. It seeks a path between nodes rather than optimizing the final route’s length. section 3.3

Common traversal rules include:

• Breadth-first search examines every node at one level before moving to the next. section 3.2
• Depth-first search follows one branch as far as possible, then backtracks to explore alternatives. section 3.6
• Depth-limited search constrains DFS with a maximum depth, while iterative deepening increases that limit until reaching the goal. section 3.2
• Bidirectional search explores forward from the start and backward from the goal at the same time. section 3.6

Where traversal helps

Apply traversal to explore a maze, solve an n-puzzle, determine relationships in a family or social graph, or find a route between an origin and destination. These searches can find feasible paths without necessarily selecting the shortest one. section 3.0 section 3.3

Figure 3.10 Graph search methods

Compare the main graph-search methods and their exploration patterns. section 3.2

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Defines traversal and distinguishes BFS, DFS, DLS, IDS, and bidirectional search, but stops short of route-length optimization.

Examples

Demonstrates traversal through mazes, n-puzzles, family trees, social graphs, and origin-to-destination routing.

What it assumes

Introduces graphs as vertices connected by edges and distinguishes traversal from shortest-path optimization.

What you can do after

Apply BFS, DFS, DLS, IDS, or bidirectional search to find paths through simple, well-defined graph problems.

How it connects

Connects graph traversal with graph search, blind search, shortest-path algorithms, breadth-first search, and depth-first search.

liveBookmanning.com

Read Optimization Algorithms: AI techniques for design, planning, and control problems on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Grokking Data StructuresArray traversal is the process of visiting the elements of an array, typically to inspect or process their values.
• 

  liveBookmanning.com
  AI-Powered SearchGraph traversal is the process of visiting nodes and relationships in a graph according to a specified path or sequence of steps.

Compare how 3 books explain traversal →

Related concepts

• breadth-first searchincludes · Breadth-first search is a graph or tree traversal algorithm that explores nodes in nondecreasing order of their distance from a starting node.
• depth-first searchincludes · Depth-first search is a graph or tree traversal algorithm that explores one branch as far as possible before backtracking to explore alternatives.
• postorder traversalincludes · A postorder traversal visits each node after visiting its children or subtrees.
• preorder traversalincludes · Preorder traversal is a tree traversal strategy that processes each node before recursively processing its child subtrees.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.