Contents

- 1 Why would you think the hill climbing algorithm is best to deal the Travelling salesman problem?
- 2 What is hill climbing approach?
- 3 How genetic algorithm is better than hill climbing approach?
- 4 Is Hill climbing greedy?
- 5 Why is hill climbing not complete?
- 6 What are the drawbacks of hill climbing algorithm?
- 7 Which is the best algorithm for solving TSP?
- 8 How does hill climbing work in stochastic optimisation?
- 9 Is the algorithm climbing a hill one step at a time?
- 10 Why does my hill climb keep getting stuck?

## Why would you think the hill climbing algorithm is best to deal the Travelling salesman problem?

Hill climbing is a mathematical optimization algorithm, which means its purpose is to find the best solution to a problem which has a (large) number of possible solutions. In the Travelling salesman problem, we have a salesman who needs to visit a number of cities exactly once, after which he returns to the first city.

## What is hill climbing approach?

In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by making an incremental change to the solution.

## How genetic algorithm is better than hill climbing approach?

Distance from hill climbing is always similar proven from 20 times testing, while the genetic algorithm to get various distance. The test based on this distance can be seen from the optimal value of the shortest track on genetic algorithm and hill climbing.

## Is Hill climbing greedy?

Since hill-climbing uses a greedy approach, it will not move to the worse state and terminate itself. The process will end even though a better solution may exist. To overcome local maximum problem : Utilize backtracking technique.

## Why is hill climbing not complete?

Hill climbing is neither complete nor optimal, has a time complexity of O(∞) but a space complexity of O(b). No special implementation data structure since hill climbing discards old nodes. Because of this “amnesy”, hill climbing is a suboptimal search strategy and hill climbing is not complete.

## What are the drawbacks of hill climbing algorithm?

Disadvantages of Hill Climbing It is not suited to problems where the value of the heuristic function drops off suddenly when the solution may be in sight. It is a local method as it looks at the immediate solution and decides about the next step to be taken rather than exploring all consequences before taking a move.

## Which is the best algorithm for solving TSP?

The goal of TSP is to find optimal route, whether it’s the lowest cost, lowest distance, fastest, etc. for more information you can consult wikipedia. Popular algorithm to solve TSP is Hill Climbing, though will not produce optimal solution for complex TSP.

## How does hill climbing work in stochastic optimisation?

Hill-climbing, pretty much the simplest of the stochastic optimisation methods, works like this: if there are no more uphill steps, stop; otherwise carry on taking uphill steps Metaphorically the algorithm climbs up a hill one step at a time.

## Is the algorithm climbing a hill one step at a time?

Metaphorically the algorithm climbs up a hill one step at a time. It is a “greedy” algorithm and only ever takes steps that take it uphill (though it can be adapted to behave differently).

## Why does my hill climb keep getting stuck?

Standard hill-climbing will tend to get stuck at the top of a local maximum, so we can modify our algorithm to restart the hill-climb if need be. This will help hill-climbing find better hills to climb – though it’s still a random search of the initial starting points.