Скачать с ютуб حل مسئله ان وزیر با استفاده از الگوریتم ژنتیک | Solving N queen problem using the genetic algorithm в хорошем качестве

حل مسئله ان وزیر با استفاده از الگوریتم ژنتیک | Solving N queen problem using the genetic algorithm

حل مسئله ان وزیر با استفاده از الگوریتم ژنتیک Solving the N queen problem using the genetics algorithm در این ویدئو ابتدا در مورد مسئله ان وزیر و سپس در مورد الگوریتم ژنتیک صحبت میکنیم. الگوریتم ژنتیک به طور کامل و بدون استفاده از ماژول اماده این الگوریتم نوشته میشود. دانلود برنامه: https://github.com/RaminSaljoughineja... Code Download Link: https://github.com/RaminSaljoughineja... Description: The N-Queens problem is a classic problem in computer science and mathematics that involves placing N chess queens on an N x N chessboard such that no two queens threaten each other. In other words, no two queens can occupy the same row, column, or diagonal. The problem is to find all possible configurations of the queens on the board that satisfy this condition. The N-Queens problem is a well-known example of a constraint satisfaction problem, which involves finding a solution that satisfies a set of constraints. It has applications in many areas, such as scheduling, resource allocation, and cryptography. The problem becomes more challenging as the value of N increases, as the number of possible configurations of the queens grows exponentially with N. Various algorithms and heuristics have been developed to solve the N-Queens problem, such as backtracking, genetic algorithms, and simulated annealing. Genetic algorithm is a search and optimization technique inspired by the process of natural selection and genetics. It works by creating a population of potential solutions to a problem and then applying genetic operators such as selection, crossover, and mutation to evolve the population towards better solutions. The genetic algorithm begins with a population of randomly generated individuals, each representing a potential solution to the problem. Each individual is evaluated using a fitness function that measures how well it solves the problem. The fitter individuals are then selected to reproduce and create offspring through crossover and mutation. Crossover involves combining the genetic material of two individuals to create new offspring that inherit some traits from each parent. Mutation involves randomly changing some of the genetic material of an individual to introduce new variation into the population. The process of selection, crossover, and mutation is repeated for multiple generations, with the fitter individuals surviving and reproducing to create a new population. Eventually, the genetic algorithm converges towards a population of individuals that are highly fit and provide good solutions to the problem. Genetic algorithm is a powerful and flexible optimization technique that can be applied to a wide range of problems. It has been used in many fields, including engineering, finance, and computer science, to find optimal solutions to complex problems. لینک سابسکرایب: https://www.youtube.com/channel/UCgbf... با زیر نویس فارسی With Persian Subtitles 📢📢📢📢📢📢📢📢📢 آدرس سایت و شبکه های اجتماعی ما:   / ramin-sal.  . https://github.com/RaminSaljoughinejad تگ ها: #raminsaljoughinejad #geneticAlgorithm #Python #geneticAlgorithmFromScratch #ژوپیتر #جوپیتر #رامین #سلجوقی نژاد