MACSANN

This is a python implementation of Memetic algorithm with crossover to search architecture of neural network (MACSANN). The objective of the algorithm is to find an architecture of an ANN that could solve a given problem.

How to use

• Configure your config file, you can see an example in ("config-example.ini")

  • Here you set the hyperparameters of your experiment:

• Create the population

  import macsann
  population = macsann.population('config-example.ini')
  

• Set train and validation data

          #Train data
          population.train_input = boston['data'][:300]
          population.train_output = boston['target'][:300]
          
          #Validation data
          population.eval_input = boston['data'][300:]
          population.eval_output = boston['target'][300:]
  

• Run your experiment and save the best population

  best_pop = population.run()
  with open('best_pop.macsann','wb') as f:
      pickle.dump(best_pop, f)
  

• See the complete example in 'boston-example.py'

#Paper https://predictori.com/macsann/paper.pdf