Classic Hill Climbing Visualization
Click anywhere on the curve to place the agent and watch hill climbing navigate the landscape!Click any scenario to see hill climbing behavior at key landscape features:
Start near the highest peak - guaranteed success!
Navigate the flat area that leads to the global maximum!
Get stuck on the smaller peak - classic failure case!
Experience plateau behavior with no clear direction!
The Goal: The highest point on the entire landscape. Hill climbing succeeds when it starts close enough to reach this peak.
Hidden Path: A flat region that eventually leads upward to the global maximum. Requires patience and multiple steps.
The Trap: Higher than immediate neighbors but not globally optimal. Hill climbing stops here, thinking it found the solution.
Plateau Effect: A flat-topped peak where many states have the same value. Algorithm can't determine direction.
This 1D visualization clearly shows why starting position matters in hill climbing. The algorithm's success depends entirely on the initial state and the local structure of the search landscape. Advanced algorithms like simulated annealing and genetic algorithms were developed to overcome these limitations.