A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598
You can install the latest version of jMetalPy with pip
,
pip install jmetalpy # or "jmetalpy[distributed]"
jMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/). These (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users): ```console pip install jmetalpy ``` This is the equivalent of running: ```console pip install "jmetalpy[core]" ``` Other supported commands are listed next: ```console pip install "jmetalpy[dev]" # Install requirements for development pip install "jmetalpy[distributed]" # Install requirements for parallel/distributed computing pip install "jmetalpy[complete]" # Install all requirements ```
Examples of configuring and running all the included algorithms are located in the documentation.
from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator.crossover import SBXCrossover
from jmetal.operator.mutation import PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = ZDT1()
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables(), distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)
algorithm.run()
We can then proceed to explore the results:
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, print_variables_to_file
front = get_non_dominated_solutions(algorithm.result())
# save to files
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
Or visualize the Pareto front approximation produced by the algorithm:
from jmetal.lab.visualization import Plot
plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
The current release of jMetalPy (v1.7.0) contains the following components:
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This project is licensed under the terms of the MIT - see the LICENSE file for details.