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The Implementation of Genetic Algorithms to Locate Highest Elevation

By Harry Beddo. The Implementation of Genetic Algorithms to Locate Highest Elevation. Basic Genetic Algorithm. Create Initial Population Pairing Mating Mutation Checking. Initial Population. Chromosome made up of 1’s and 0’s Large initial population. Pairing. Simple pairing

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The Implementation of Genetic Algorithms to Locate Highest Elevation

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  1. By Harry Beddo The Implementation of Genetic Algorithms to Locate Highest Elevation

  2. Basic Genetic Algorithm • Create Initial Population • Pairing • Mating • Mutation • Checking

  3. Initial Population • Chromosome made up of 1’s and 0’s • Large initial population

  4. Pairing • Simple pairing • Random pairing • Random weighted pairing • Tournament style pairing

  5. Mating • Random selection point • Crossover

  6. Mutations • Change a 1 to a 0 and visa versa • Only 5%

  7. Checking • Reached iteration limit • Convergence • Stops or goes back to pairing step

  8. Results • Find highest elevation • Limit search area

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