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Merge pull request #30 from jMetal/develop
Minor release 0.5.1
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‎.travis.yml‎

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python: '3.6'
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install: pip install -r requirements.txt
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script: python -m unittest discover
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branches:
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only:
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- master
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- "/.*/"
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deploy:
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provider: pypi
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skip_cleanup: true
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user: ajnebro
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user: benhid
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distributions: sdist bdist_wheel
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password:
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secure: 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secure: 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on:
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branch: master

‎CONTRIBUTING.md‎

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‎README.md‎

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- [Installation](#installation)
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- [Usage](#usage)
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- [Features](#features)
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- [Contributing](#contributing)
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- [License](#license)
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## Installation
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Examples of configuring and running all the included algorithms are located [in the docs](https://jmetalpy.readthedocs.io/en/latest/examples.html).
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## Features
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The current release of jMetalPy (v0.5.0) contains the following components:
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* Algorithms: random search, NSGA-II, SMPSO, SMPSO/RP
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* Problems: ZDT1-6, DTLZ1-2, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
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* Encodings: real, binary
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* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random)
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* Quality indicators: hypervolume
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* Density estimator: crowding distance
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* Graphics: 2D/3D plotting in real-time
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## Contributing
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Please read [CONTRIBUTING](CONTRIBUTING.md) for details on how to contribute to the project.
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The current release of jMetalPy (v0.5.1) contains the following components:
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* Algorithms: random search, NSGA-II, SMPSO, SMPSO/RP.
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* Benchmark problems: ZDT1-6, DTLZ1-2, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
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* Encodings: real, binary.
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* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
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* Quality indicators: hypervolume.
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* Density estimator: crowding distance.
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* Graphics: Pareto front plotting (2 or more objectives).
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* Laboratory: Experiment class for performing studies.
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<p align="center">
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<br/>
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<img src=docs/source/visualization.png alt="Visualization">
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<br/>
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</p>
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## License
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This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.

‎docs/source/NSGAII-ZDT1.png‎

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‎docs/source/about.rst‎

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--------------------------------
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1. J.J. Durillo, A.J. Nebro jMetal: a Java Framework for Multi-Objective Optimization. Advances in Engineering Software 42 (2011) 760-771.
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2. A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
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2. A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
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3. Nebro A.J. et al. (2018) Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation. In: Auger A., Fonseca C., Lourenço N., Machado P., Paquete L., Whitley D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science, vol 11101. Springer, Cham

‎docs/source/api/jmetal.util.rst‎

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Utils
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===================
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Front file
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-----------------------------
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.. automodule:: jmetal.util.front_file
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:members:
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:undoc-members:
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:show-inheritance:
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Graphic
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--------------------------
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‎docs/source/conf.py‎

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# http://blog.rtwilson.com/how-to-make-your-sphinx-documentation-compile-with-readthedocs-when-youre-using-numpy-and-scipy/
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import mock
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MOCK_MODULES = ['numpy', 'dask', 'dask.distributed', 'tqdm', 'bokeh', 'bokeh.embed', 'bokeh.front', 'bokeh.client',
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'bokeh.io', 'bokeh.layouts','bokeh.models','bokeh.plotting', 'bokeh.resources',
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'mpl_toolkits', 'mpl_toolkits.mplot3d', 'matplotlib', 'matplotlib.pyplot', 'matplotlib.axes']
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MOCK_MODULES = ['numpy', 'pandas',
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'tqdm',
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'plotly', 'plotly.offline',
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'matplotlib', 'matplotlib.pyplot', 'mpl_toolkits', 'mpl_toolkits.mplot3d']
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for mod_name in MOCK_MODULES:
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sys.modules[mod_name] = mock.Mock()
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‎docs/source/examples.rst‎

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Examples
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==============
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Getting started
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==================
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.. toctree::
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:maxdepth: 2
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:caption: Algorithms:
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runner/nsgaii
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runner/smpso
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examples/nsgaii
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examples/smpso
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.. toctree::
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:maxdepth: 2
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:caption: Further configuration:
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runner/observer
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examples/observer
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examples/experiment

‎docs/source/examples/experiment.rst‎

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Experiments
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========================
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This is an example of an experimental study based on solving two problems of the ZDT family with two versions of the same algorithm (NSGAII).
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The hypervolume indicator is used for performance assessment.
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.. code-block:: python
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# Configure experiment
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problem_list = [ZDT1(), ZDT2()]
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algorithm_list = []
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for problem in problem_list:
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algorithm_list.append(
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('NSGAII_A',
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NSGAII(
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problem=problem,
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population_size=100,
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max_evaluations=25000,
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mutation=NullMutation(),
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crossover=SBX(probability=1.0, distribution_index=20),
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selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
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))
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)
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algorithm_list.append(
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('NSGAII_B',
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NSGAII(
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problem=problem,
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population_size=100,
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max_evaluations=25000,
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mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20),
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crossover=SBX(probability=1.0, distribution_index=20),
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selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
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))
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)
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study = Experiment(algorithm_list, n_runs=2)
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study.run()
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# Compute quality indicators
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metric_list = [HyperVolume(reference_point=[1, 1])]
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print(study.compute_metrics(metric_list))

‎docs/source/examples/nsgaii.rst‎

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