How should I unit test a heuristic algorithm?

How should I unit test a heuristic algorithm?

By : pouya yousefi
Date : November 21 2020, 04:03 PM
hope this fix your issue I test my TSP implementation with this integration test class, which does 2 tests:
Asserts that it reaches a certain score within 600 seconds. I get that score within 10 seconds on my machine, so the long timeout is only for really slow jenkins slaves. If it doesn't reach that score in that time limit, it's probably never going to reach it. The point of this test is that no Exceptions are thrown (= smoke test) and that at least it improves the score in reasonable time. So it's better than no test :) Puts the solver in an assertionMode (FAST_ASSERT) and asserts that it reaches a certain, easier score within 600 seconds. In assertionMode, the Solver enables sanity checks in it's deepest loops (at a performance cost). This is to flush out bugs in incremental score calculation (= delta score calculation) etc.
code :

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Heuristic value in A* algorithm

Heuristic value in A* algorithm

By : Meghna Lohani
Date : March 29 2020, 07:55 AM
Any of those help In order to get a heuristic that estimates (lower bounds) the minimum path cost between two nodes there are two possibilities (that I know of):
Knowledge about the underlying space the graph is part of
Why greedy algorithm is heuristic, not meta-heuristic?

Why greedy algorithm is heuristic, not meta-heuristic?

By : user3628641
Date : March 29 2020, 07:55 AM
Hope this helps There are a lot of greedy algorithms for different problems, greedy algorithm is not the one particular algorithm, it's a class of algorithms that use the same approach to the problem. Dijkstra's algorithm, Prim's algorithm, Kruskal's algorithm, etc. are completely different, but they are all greedy.
In Dijkstra's algorithm you take an untouched node with minimal distance to it. In Prim's algorithm you take an edge, that connects tree node with not-tree node, with minimal weight. In Kruskal's algorithm you take an edge, that connects two different trees, with minimal weight. And there are many greedy algorithms that don't even work with graphs.
Heuristic and A* algorithm

Heuristic and A* algorithm

By : rathna kumar
Date : March 29 2020, 07:55 AM
it should still fix some issue In this context, a heuristic is a way of providing the algorithm with some form of extra evaluative information, so that the algorithm can find a 'good enough' solution, without exhaustively searching every possible solution.
Dijkstra's Algorithm does not use a heuristic. It expands outwards from the start node, and examines every node in the graph in order to find the shortest path. While this is accurate, it can be computationally expensive.
Heuristic For A* Algorithm

Heuristic For A* Algorithm

By : Łukasz Grzesło
Date : March 29 2020, 07:55 AM
With these it helps A good first-pass search heuristic is to use a greedy algorithm. For example, in a general route-planning algorithm (find the shortest route between cities) a decent heuristic is to use a greedy algorithm where you always go to the next city that's closest to the destination as the crow flies; this is a linear-time heuristic and never overestimates the solution. In your case, maybe you can use a greedy algorithm in which a garbage truck always goes to the next-closest garbage node, or the garbage node with the most garbage; I can't get more specific without knowing the details of the four nodes you're using, but you get the idea. Any linear-time algorithm that doesn't overestimate the solution will do, and you can then tweak it in your next pass. (An nlog(n) heuristic is also acceptable in most cases; n^2 is getting awfully expensive.)
How do you unit test an algorithm in C#?

How do you unit test an algorithm in C#?

By : Hari Thapa
Date : March 29 2020, 07:55 AM
This might help you Use a table of inputs and known outputs as you described. You need to get the outputs from another implementation of the same algorithm from a source you know is accurate.
If you're implementing an algorithm that doesn't have readily available input/output data, then re-implement the algorithm another way, such as in Excel, and generate data you know to be accurate. We do this often with statistical calculations for reports where we can generate data easily in Excel.
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