simple-genetic-algorithm 0.2.0

Genetic Algorithm Framework

Genetic-Algorithm

Implementation of Genetic-Algorithm for solution finding (optimization)

Easy to use GA implementation. With parallel computing and info-prints. Simple and flexible for your optimal solution finding.

Usage

1. Download the project and add to python module search path

   import sys
   sys.path.insert(0, '../path_to_GA_py_dir')
   # ./ => this folder
   # ../ => supfolder
   

2. Or pip install it (easier)

   pip install Simple-Genetic-Algorithm
   

3. Import the class and helper function

   from genetic_algorithm import GA, get_random
   

4. Create 2 functions and parameters

   class Example_GA(GA):
   
       def calculate_fitness(self, kwargs, params):
           # return here the fitness (how good the solution is)
           # as bigger as better!
           # hint: if you have a loss, you should propably just return -1*loss
           # example for sklearn model:
            model = RandomForestRegressor(n_estimators=params["n_estimators"], ...)
     		 model = model.fit(kwargs["X_train"], kwargs["y_train"])
           # predict
           y_pred = model.predict(kwargs["X_test"])
           # calc mean absolute error loss
           y_true = np.array(kwargs["y_test"])
           y_pred = np.array(y_pred)
           return - np.mean(np.abs(y_true - y_pred))
   
       def get_random_value(self, param_key):
           if param_key == "name_of_parameter_1":
               return get_random(10, 1000)
           elif param_key == "name_of_parameter_2":
               return get_random(["something_1", "something_2", "something_3"])
           elif param_key == "name_of_parameter_3":
               return get_random(0.0, 1.0)
           
           ...
   
   parameters = ["n_estimators", "criterion", "max_depth", "max_features", "bootstrap"]
   

5. Create and run genetic algorithm and pass the input, which will be used in the calculate_fitness function

   optimizer = Example_GA(generations=10, population_size=15, mutation_rate=0.3, list_of_params=parameters)
   optimizer.optimize(X_train=X_train, y_train=y_train, X_test=X_dev, y_test=y_dev)
   

For more examples, see:

• Regression with RandomForrestRegressor
• Knapsack problem

License

Feel free to use it. Of course you don't have to name me in your code :)

-> It is a copy-left license

For all details see the license file.