Solving N-Queens problem using Genetic Algorithms

Topics: Genetic algorithm / Pages: 23 (4307 words) / Published: Nov 28th, 2004
1 Introduction

The N-Queens problem is a classical AI problem. Its name is derived from the allowed moves for the queen piece in chess. Queens are allowed to move horizontally, vertically, or diagonally, backward and forward, with the only restriction being that they can move in only one direction at a time. A queen that can reach another piece in one move captures it.

The N-Queens problem is based on the notion of trying to place N queens on an N x N grid, such that no queen will be able to capture any other queen. The N-queens problem is typical of many combinatorial problems, in that it is simple to state and relatively easy to solve for small N, but becomes difficult with a large N. There are few ways to solve the N-queens problem. Some of them are trying all the permutations, using backtracking methods, using reinforcement learning methods, and etc. In this project, genetic algorithm will be used to solve this problem by using GAlib package.

Genetic Algorithms are adaptive methods which may be used to solve search and optimization problems. They are based on the genetic processes of biological organisms. Over many generations, natural populations evolve according to the principles of natural selection and "survival of the fittest". By mimicking this process, genetic algorithms are able to "evolve" solutions to real world problems, if they have been suitably encoded.

Genetic Algorithms use a direct analogy of natural behavior. They work with a population of "individuals", each representing a possible solution to a given problem. Each individual is assigned a "fitness score" according to how good a solution to the problem it is. The highly fit individuals are given opportunities to "reproduce", by "cross breeding" with other individuals in the population. This produces new individuals known as "offsprings", which share some features taken from each "parent". The least fit members of the population are less likely to get selected for reproduction, and so will

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