v0.8.1

0.8.1, 10/10/2023

• New Features

  • Added asynchronous NSGA-II, which is in leap_ec.distrib.asynchronous;
    note that the API may change in the future

• API changes

  • Added CGPDecoder.initialize() method for convenience, offering a default genome initializer
  • Replaced n_ary_crossover and uniform_crossover functions with classes NAryCrossover and UniformCrossover
  • Added a persist_children flag to crossover operators, which allows offspring pairs
    to be used with steady-state algorithms
  • Added a uuid field to the Individual base class, and Individual now also tracks parent & offspring UUIDs; this
    moved UUID support from DistributedIndividual
  • Added a parents attribute to Individual base class that tracks the
    UUIDs of the parents via clone or crossover
  • Improved auto-scaling of axes for PopulationMetricsPlotProbe and FitnessPlotProbe
  • standardized on parameter name bounds for mutation operators; previously was inconsistent nomenclature between
    hard_bounds and bounds
  • Made improvements to ReadTheDocs documentation.

v0.8.0

0.8.0, 4/14/2023

• New Features

  • Added FitnessOffsetProblem convenience wrapper to the problem module
  • Added ParabaloidProblem and QuadraticFamilyProblem to the real_rep.problems module
  • CGP now supports auxiliary constant parameters on each node via CGPWithParametersDecoder
  • Added ImageXYProblem to executable_rep.problems, and a cgp_images.py example demonstrating it
  • Added experimental parameters to mutate_gaussian() to allow transforming genes by a linear function
  • Added a check_constraints() operator to the CGPDecoder class, to help verify custom algorithms
  • Added LeadingOnes, DeceptiveTrap, and TwoMax problems to binary_rep.problems module
  • Added SumPhenotypePlotProbe, and a new example using it to visualizing MaxOnes-style problems
  • Added multiobjective sub-package that provides support for NSGA-II
    • multiobjective.nsga2.nsga2() top-level monolithic function
    • multiobjective.problems.MultiObjectiveProblem is new abstract base class for multiobjective problems
    • multiobjective.ops contains supporting pipeline operators, though most users will not see those if they use nsga()

• API changes

  • Individual now has a phenome property
  • Mutation operators (mutate_gaussian() and mutate_binomial()) can now be passed a list of std values to adjust the mutation width by gene.
  • Removed an undocumented normalization term from real_rep.problems.CosineFamilyProblem
  • Expose a reset method on PopulationMetricsPlotProbe
  • util.inc_generation() now takes a start_generation argument
  • genome_mutate_gaussian() is now a curried function instead of a closure
  • plot_2d_problem() and plot_2d_function() now accept extra kwargs to forward to Matplotlib
  • MaxOnes now takes an optional target_string to generalize it to other target patterns

v0.7.0

0.7.0, 8/5/2021

• New features

  • Added ops.sus_selection() and ops.proportional_selection()

• API changes

  • Made numpy arrays (instead of lists) the default representation for most LEAP operators and examples, for a significant speedup.
  • Added indices parameter to ops.random_selection()
  • plot_2d_problem() now defaults to checking the problem.bounds field for xlim and ylim values
  • ea_solve() now accepts optional Dask Client object to enable
    parallel evaluations
  • generational_ea() now supports elitism by default

v0.6.0

v0.6.0, 6/13/2021

• Drop support for Python 3.6

  • This keeps us in sync with numpy and dask that also dropped support for 3.6 this year

• New features

  • Added landscape_features package with some initial exploratory landscape analysis tools
  • Added elitism
  • Added a new example demonstrating integer representations
  • Added a mutate_binomial() operator for integer representations
  • Added visualization of ANN weights for SimpleNeuralNetworkExecutable phenotypes
  • Added metrics for logging population diversity
  • Added support for lexicographical and Koza-style parsimony pressure
  • Added HistPhenotypePlotProbe
  • Added ops.grouped_evaluate() for evaluating batches of individuals
  • Added ExternalProcessproblem for using external programs as fitness functions

• Documentation

  • Added documentation on leap_ec.context and updated software development
    guidelines to encourage its use if tracking persistent state outside of
    function calls was necessary.

• CI/CD

  • Added a make test-slow harness
  • Added tests that run the examples/ scripts
  • Organized examples into subdirectories
  • Improved test coverage

• Bugfixes

  • Fix viz parameter when calling simple.ea_solve()
  • Fix algebra error in real_rep.problems.NoisyQuarticProblem
  • Tell dask that functions are impure by default, to make sure it doesn't cache results
  • Change Makefile to use pip install -e . instead of the deprecated python setup.py develop

• API changes

  • Significantly refactored the executable_rep.rules package to simplify learning classifier systems
  • Added leap_ec.__version__ attribute
  • Added a hard_bounds flag to ea_solve() to tell it to respect the bounds at all times (rather than just initialization); defaults to True
  • Added the most frequent imports (ex. Individual, Representation) into the top-level package
  • Renamed the generations parameter of generational_ea() to max_generations and added an optional stop parameter for other stopping conditions
  • Added probability parameter for the uniform_crossover operator
  • mutate_gaussian now accepts a list of gene-wise hard bound
  • Added select_worst Boolean parameter to tournament_selection
  • Added notes columns parameter to FitnessStatsCSVProbe
  • Added a pad_inputs parameter to TruthTableProblem to handle varying-dimension inputs
  • Added a pad parameter to CartesianPhenotypePlotProbe to plot 2D projections of higher-D functions
  • Added FitnessPlotProbe as a convenience wrapper for PopulationMetricsPlotProbe
  • Added an x_axis_value parameter to FitnessPlotProbe and PopulationMetricsPlotProbe
  • Renamed PlotTrajectoryProbe to the more descriptive CartesianPhenotypePlotProbe
  • Renamed PopulationPlotProbe to the more descriptive PopulationMetricsPlotProbe
  • Renamed leap_ec.distributed to leap_ec.distrib to reduce name space
    confusion with dask.distributed
  • Renamed leap_ec.context to leap_ec.global_vars
  • Default behavior changes
    • Individual.decoder and Representation.decoder now uses a phenotypic representation (IdentityDecoder) by default
    • Mutation operators no longer have default mutation rates (they must be explicitly set by the user).
    • Set default p_swap = 0.2 for uniform_crossover, instead of 0.5
    • Set default num_points = 2 for n_ary_crossover, instead of 1
    • Set default value for context parameter on probes, so users needn't set it
    • standardized on making context last function argument that defaults to
      leap_ec.context.context

0.5.0, 1/9/2021

• Added probability parameter for the n_ary_crossover operator
• Greatly improved test coverage
• Added support for static- and variable-length segments, which are fixed-length "chunks" of values
• Added a simple neural network representation, executable_rep.neural_network, and made it the default for examples/openai_gym.py
• Changed the Executable interface to act as a Callable object (rather than using a custom output() method)
• Added statistical_helpers to assist with writing unit tests for stochastic algorithms
• Added support for integer representations, via the int_rep package
• Added a Cartesian genetic programming (CGP) representation, executable_rep.cgp, with example in examples/cgp.py
• Added support for heterogeneous island models, demoed in examples/multitask_island_model.py

Release 0.4.0

Release 0.4.0, 9/19/2020

• Significantly added to online documentation
• Major code reorganization
  • exception management for Individual has been moved to RobustIndividual
  • DistributedIndividual now inherits from RobustIndividual
  • core.py has been broken out to separate modules
    • Individual and RobustIndividual now in individual.py
    • representation specific entities moved to new sub-packages, binary_rep
      and real_rep
    • Representation now in representation.py
    • Decoder now in decoder.py
  • documentation, doctests, examples, Jupyter notebooks, and unit tests updated accordingly
• added ability to pass ancillary information during evaluation, such as UUIDs
  that could be used to name output files and directories, yet do not have a
  direct impact on fitness

LEAP v0.3.0

A minor release, consisting mostly of code cleaning and documentation.

LEAP v0.2.0

An initial release, corresponding to the version of LEAP described in our GECCO 2020 workshop paper.