Genetic Algorithms
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Genetic algorithms (GAs) are a framewrok to solve optimiaztion and decision problems, inspired in darwin
evolutionary concetps, uses different ideas.


.. important:: 
    The `Slides <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Genetic-Beamer.pdf>`_




`Slides <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Genetic-Beamer.pdf>`_
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Options
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Laboratories
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All problems listed in this section was resolver using ``GAs`` and
``deap``

* In python:
  
  * `Optimization univariate Function  <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Optimization(ScalarFunction_Genetic)(BIT).ipynb>`_
  * `Genetic Multivariate linear regression <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Genetic_Linear_Regression_(BIT).ipynb>`_
  * `Ksnap problem <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/knapsack(Genetic)(BIT).ipynb>`_
  * `Feature Selection joined with sklearn <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/FeatureSelectionGenetic(BIT).ipynb>`_
  * `Training a Multilayer Perceptron (one hidden layer) <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Genetic_One_hidden_Multilayer.ipynb>`_
  * `Travelling salesman problem (TSP) <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Genetic_TSP(BIT).ipynb>`_
  * `Genetic K-means <https://github.com/it-ces/PUBLIC-AI/blob/main/OPTIMIZATION/Genetic_K_means(ai_page).ipynb>`_








stoppong criteria
-----------------------------

One of the most fundamental issues in GA is when stop given that there are not
a stablished criteria.