GAFT

A Genetic Algorithm Framework in pyThon

Introduction

gaft is a Python Framework for genetic algorithm computation. It provide built-in genetic operators for genetic algorithm optimization and plugin interfaces for users to define your own genetic operators and on-the-fly analysis for algorithm testing.

gaft is now accelerated using MPI parallelization interfaces. You can run it on your cluster in parallal with MPI environment.

Installation:

Via pip:
```
pip install gaft
```
From source:
```
python setup.py install
```

Example:

1. Importing

from gaft import GAEngine
from gaft.components import GAIndividual
from gaft.components import GAPopulation
from gaft.operators import RouletteWheelSelection
from gaft.operators import UniformCrossover
from gaft.operators import FlipBitMutation

# Analysis plugin base class.
from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis

2. Define population

indv_template = GAIndividual(ranges=[(0, 10)], encoding='binary', eps=0.001)
population = GAPopulation(indv_template=indv_template, size=50)

3. Create genetic operators

# Use built-in operators here.
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=0.8, pe=0.5)
mutation = FlipBitMutation(pm=0.1)

4. Create genetic algorithm engine to run optimization

engine = GAEngine(population=population, selection=selection,
                  crossover=crossover, mutation=mutation,
                  analysis=[FitnessStoreAnalysis])

5. Define and register fitness function

@engine.fitness_register
def fitness(indv):
    x, = indv.variants
    return x + 10*sin(5*x) + 7*cos(4*x)

6. Define and register an on-the-fly analysis (optional)

@engine.analysis_register
class ConsoleOutputAnalysis(OnTheFlyAnalysis):
    interval = 1
    def register_step(self, ng, population, engine):
        best_indv = population.best_indv(engine.fitness)
        msg = 'Generation: {}, best fitness: {:.3f}'.format(ng, engine.fitness(best_indv))
        engine.logger.info(msg)

7. Run

if '__main__' == __name__:
    engine.run(ng=100)

8. Evolution curve

9. Optimization animation

See example 01 for a one-dimension search for the global maximum of function f(x) = x + 10sin(5x) + 7cos(4x)

Global maximum search for binary function

See example 02 for a two-dimension search for the global maximum of function f(x) = y*sin(2*pi*x) + x*cos(2*pi*y)

Plugins

You can define your own genetic operators for GAFT and run your algorithm test.

The plugin interfaces are defined in /gaft/plugin_interfaces/, you can extend the interface class and define your own analysis class or genetic operator class. The built-in operators and built-in on-the-fly analysis can be treated as an official example for plugins development.

TODO

✅ Parallelization
🏃 Add more built-in genetic operators with different algorithms (-)
🏃 Add C++ backend (-)

indieyourself / gaft