Usage¶

To use pyeasyga in a project:

Simple¶

Import the module
from pyeasyga import pyeasyga
Setup your data e.g.
data = [('pear', 50), ('apple', 35), ('banana', 40)]
Initialise the GeneticAlgorithm class with the only required parameter: data
ga = pyeasyga.GeneticAlgorithm(data)
Define a fitness function for the Genetic Algorithm. The function should take two parameters: a candidate soultion (an individual in GA speak), and the data that is used to help determine the individual’s fitness
def fitness (individual, data):
    fitness = 0
    if individual.count(1) == 2:
        for (selected, (fruit, profit)) in zip(individual, data):
            if selected:
                fitness += profit
    return fitness
Set the Genetic Algorithm’s fitness_function attribute to your defined fitness function
ga.fitness_function = fitness
Run the Genetic Algorithm
ga.run()
Print the best solution
print ga.best_individual()

Advanced¶

Import the module
```
from pyeasyga import pyeasyga
```

Setup your data e.g.

data = [('pear', 50), ('apple', 35), ('banana', 40)]

Initialise the GeneticAlgorithm class with the required data parameter, and all or some of the optional parameters

ga = pyeasyga.GeneticAlgorithm(data,
                               population_size=10,
                               generations=20,
                               crossover_probability=0.8,
                               mutation_probability=0.05,
                               elitism=True,
                               maximise_fitness=True)

Or

ga = pyeasyga.GeneticAlgorithm(data, 10, 20, 0.8, 0.05, True, True)

Or, just initialise the GeneticAlgorithm class with only the required data parameter, if you are content with the default parameters

ga = pyeasyga.GeneticAlgorithm(data)

Optionally, define a function to create a representation of a candidate solution (an individual in GA speak). This function should take in the data defined in step 1. as a parameter.
```
def create_individual(data):
    return [random.randint(0, 1) for _ in xrange(len(data))]
```
Set the Genetic Algorithm’s create_individual attribute to your defined function
```
ga.create_individual = create_individual
```

Optionally, define and set functions for the Genetic Algorithm’s genetic operators (i.e. crossover, mutate, selection)

# For the crossover function, supply two individuals (i.e. candidate
# solution representations) as parameters,
def crossover(parent_1, parent_2):
    crossover_index = random.randrange(1, len(parent_1))
    child_1 = parent_1[:index] + parent_2[index:]
    child_2 = parent_2[:index] + parent_1[index:]
    return child_1, child_2

# and set the Genetic Algorithm's ``crossover_function`` attribute to
# your defined function
ga.crossover_function = crossover


# For the mutate function, supply one individual (i.e. a candidate
# solution representation) as a parameter,
def mutate(individual):
    mutate_index = random.randrange(len(individual))
    if individual[mutate_index] == 0:
        individual[mutate_index] = 1
    else:
        individual[mutate_index] = 0

# and set the Genetic Algorithm's ``mutate_function`` attribute to
# your defined function
ga.mutate_function = mutate


# For the selection function, supply a ``population`` parameter
def selection(population):
    return random.choice(population)

# and set the Genetic Algorithm's ``selection_function`` attribute to
# your defined function
ga.selection_function = selection

Define a fitness function for the Genetic Algorithm. The function should take two parameters: a candidate solution representation (an individual in GA speak), and the data that is used to help determine the individual’s fitness

def fitness (individual, data):
    fitness = 0
    if individual.count(1) == 2:
        for (selected, (fruit, profit)) in zip(individual, data):
            if selected:
                fitness += profit
    return fitness

Set the Genetic Algorithm’s fitness_function attribute to your defined fitness function
```
ga.fitness_function = fitness
```
Run the Genetic Algorithm
```
ga.run()
```
Print the best solution:
```
print ga.best_individual()
```

You can also examine all the individuals in the last generation:

for individual in ga.last_generation():
    print individual

Example of Simple Usage¶

This simple example uses the default pyeasyga.GeneticAlgorithm parameters.

The problem is to select only two items from a list (the supplied data) while maximising the cost of the selected items. (Solution: Selecting the pear and apple gives the highest possible cost of 90.)

>>> from pyeasyga.pyeasyga import GeneticAlgorithm
>>>
>>> data = [('pear', 50), ('apple', 35), ('banana', 40)]
>>> ga = GeneticAlgorithm(data)
>>>
>>> def fitness (individual, data):
>>>     fitness = 0
>>>     if individual.count(1) == 2:
>>>         for (selected, (fruit, profit)) in zip(individual, data):
>>>             if selected:
>>>                 fitness += profit
>>>     return fitness
>>>
>>> ga.fitness_function = fitness
>>> ga.run()
>>> print ga.best_individual()

Example of Advanced Usage¶

This example uses both default and optional pyeasyga.GeneticAlgorithm parameters.

The problem is to select only two items from a list (the supplied data) while maximising the cost of the selected items. (Solution: Selecting the pear and apple gives the highest possible cost of 90.)

>>> from pyeasyga.pyeasyga import GeneticAlgorithm
>>>
>>> data = [('pear', 50), ('apple', 35), ('banana', 40)]
>>> ga = GeneticAlgorithm(data, 20, 50, 0.8, 0.2, True, True)
>>>
>>> def create_individual(data):
>>>     return [random.randint(0, 1) for _ in xrange(len(data))]
>>>
>>> ga.create_individual = create_individual
>>>
>>>
>>> def crossover(parent_1, parent_2):
>>>     crossover_index = random.randrange(1, len(parent_1))
>>>     child_1 = parent_1[:index] + parent_2[index:]
>>>     child_2 = parent_2[:index] + parent_1[index:]
>>>     return child_1, child_2
>>>
>>> ga.crossover_function = crossover
>>>
>>>
>>> def mutate(individual):
>>>     mutate_index = random.randrange(len(individual))
>>>     if individual[mutate_index] == 0:
>>>         individual[mutate_index] = 1
>>>     else:
>>>         individual[mutate_index] = 0
>>>
>>> ga.mutate_function = mutate
>>>
>>>
>>> def selection(population):
>>>     return random.choice(population)
>>>
>>> ga.selection_function = selection
>>>
>>> def fitness (individual, data):
>>>     fitness = 0
>>>     if individual.count(1) == 2:
>>>         for (selected, (fruit, profit)) in zip(individual, data):
>>>             if selected:
>>>                 fitness += profit
>>>     return fitness
>>>
>>> ga.fitness_function = fitness
>>> ga.run()
>>> print ga.best_individual()
>>>
>>> for individual in ga.last_generation():
>>>     print individual