Genetic Algorithm best for ai students.pptx

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1. A Genetic Algorithm (GA) is a search heuristic inspired by the process of natural selection. It is used to find approximate solutions to optimization and search problems.

2. Initialization: Generate an initial population of chessboard configurations (representing queen placements). Fitness Function: Define a fitness function to evaluate each configuration based on the number of conflicting pairs of queens. Selection: Select individuals from the population based on their fitness. Higher fitness individuals have a higher chance of being selected.

3. Crossover (Recombination): Pair selected individuals and perform crossover to create new individuals. Exchange genetic information to create offspring. Mutation: Introduce random changes to some individuals in the population. This can involve moving a queen to a different position. Replacement: Replace the old population with the new population. Termination: Repeat steps 3-6 for a certain number of generations or until a satisfactory solution is found.

4. The 8-Queens problem is a classic problem in computer science and artificial intelligence. The goal is to place eight queens on an 8x8 chessboard in such a way that no two queens threaten each other. This means that no two queens can be in the same row, column, or diagonal.