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Optimization_Problems

Monte Calro Algorithm
Las Vegas Algorithm
Best First Search - Beam Search
A* / Hill Climibing
Simulated Annealing / Tabu Search
Guided Local Search
Zobrist Hasing

How to make neighborhoods and how to evaluate solutions are important. SA: The temperature has role to control probability function. At first of program execution, it should be enough high to be able to leave a local minimum. At last of execution, it should be enough low to spend much time to search around the global minimum.

In a simulated annealing, there are basically two probability functions:

  1. one used to choose a neighborhood,
  2. one to decide whether accept a bad neighborhood or don't.

The most important thing in MM is observing the problem and making good problem-specific-heuristics.
I think we should learn many nice instances of greedy algorithm, rather than many meta-heuristics.