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Genetic Algorithm Program Flow and Architecture

Learn through exercises on mimetic learning with Francesco, Niccolo, Miguel; generate populations, mutate, evaluate fitness, and configure program architecture.

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Genetic Algorithm Program Flow and Architecture

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  1. Apprendimento mimetico Exercise 1 Francesco Abate Niccolo` Battezzati Miguel Kaouk

  2. EP – Program Flow μ Generate first population μ + μ σ, c Generate new population by mutation .MAX_ITER q NO Selection by tournament Max iterations? YES Fitness evaluation Goal? NO END YES

  3. EP – Program Architecture ep.conf EvoConfigParser booltermination(bool (*terminationFnc)(EvoAgent *)) float evaluate_fitness(float (*fitnessFnc)(EvoAgent *)) EvoAgent ( float x[D] ) EvoConfigurator main

  4. Experimental results D = 2, static σ

  5. Experimental results D = 2, dynamic σ

  6. Experimental results D = 5, dynamic σ

  7. Experimental results D = 10, dynamic σ

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