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All Projects → selimfirat → ai-n-queens

selimfirat / ai-n-queens

Licence: other
Solving and GUI demonstration of traditional N-Queens Problem using Hill Climbing, Simulated Annealing, Local Beam Search, and Genetic Algorithm.

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aigenetic-algorithmartificial-intelligencebeam-searchsimulated-annealinghill-climbingoptimization-algorithmsn-queensdiscrete-optimization

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N-Queens Problem

N-Queens is a combinatorial problem based on chess. Regarding queens can attack vertically, horizontally, and diagonally in chess, N-Queens problem asks:

How can N queens placed on an NxN chessboard so that no two of them attacks each other?

This project demonstrates the n-queens problem solution and solves them via Hill Climbing, Simulated Annealing, Local Beam Search, and Genetic Algorithm.

Demonstration

N Queens Problem

Parameters

General

  • Number of queens: Number of queens in chessboard. Namely, N.

Note: Starts with a random state(chessboard configuration).

Hill Climbing

The hill climbing algorithm gets its name from the metaphor of climbing a hill.

  • Max number of iterations: The maximum number of iterations. Each iteration is at one step higher than another.

Note: If gets stuck at local maxima, randomizes the state.

Simulated Annealing

The annealing algorithm attempts to tease out the correct solution by making risky moves at first and slowly making more conservative moves.

  • Max number of iterations: The number of times that annealing move occures.
  • Temperature: The starting temperature that affects the annealing move(randomizing). If temperature is high, the state acts more randomly(anneals). And vice versa.
  • Cooling factor: The cooling parameter that reduces the temperature parameter.

Local beam search

  • Max number of iterations: The maximum number of iterations for search. Namely, the depth of search.
  • Number of states: Number of states in beam

Note: If gets stuck at local maxima, randomizes the state.

Genetic Algorithm

Evolution-like algorithm that suggests the survival of the best ones from many combinated&unified population in each generation.

  • Initial population size: Initial population size.
  • Mutation probability: Probability of occurance of mutation in each generation.
  • Number of generations: The number of iterations to get solution.

References

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