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Recent Releases of eckity

eckity - EC-KitY 0.4.0

We are proud to annouce the release of EC-KitY 0.4.0!

This version includes many changes, with the most significant one being Typed GP.

What's Changed

  • termination_checkers: fixed mistake in comments by @ZvikaZ in https://github.com/EC-KitY/EC-KitY/pull/82
  • use logging instead of simple 'print's by @ZvikaZ in https://github.com/EC-KitY/EC-KitY/pull/66
  • add shorter imports by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/99
  • fixes in Fitness classes by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/101
  • terminationchecker: added bestfitnessstagnationtermination_checker by @ZvikaZ in https://github.com/EC-KitY/EC-KitY/pull/73
  • Fixed typo in functions.py by @jack-powers in https://github.com/EC-KitY/EC-KitY/pull/96
  • Add update-docs CI/CD by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/107
  • MOE: handle corner cases (empty list, zerodivision) by @ZvikaZ in https://github.com/EC-KitY/EC-KitY/pull/61
  • SubtreeMutation bugfix, run-examples CI/CD, requirements update by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/104
  • Update parameter replacement regex by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/108
  • Add random detector by @achiyae in https://github.com/EC-KitY/EC-KitY/pull/109
  • MOE: add statistics/moebestworst_statistics.py by @ZvikaZ in https://github.com/EC-KitY/EC-KitY/pull/62
  • moe: enhance nsga2_plot with axis names and saving by @ZvikaZ in https://github.com/EC-KitY/EC-KitY/pull/64
  • Add support for Python 3.11 by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/112
  • MOEBestWorstStatistics by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/110
  • version-detector workflow by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/115
  • Add warning for ElitismSelection with zero elites by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/113
  • Simple objects no longer iterate over all sub populations by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/105
  • Update support for random generator object by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/114
  • FP selection, arity validation in Algorithm constructor, no-replacement option in tournament selection by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/116
  • parents and update_parents fields for Individual by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/123
  • GA mutations and crossovers docs by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/125
  • changed IntVectorOnePointMutation to be more similar to the bit vecto… by @eliadsbgu in https://github.com/EC-KitY/EC-KitY/pull/129
  • Remove default termination by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/130
  • rename MOE classes that contain the word 'test' by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/134
  • Typed GP by @itaitzruia4 in https://github.com/EC-KitY/EC-KitY/pull/131

New Contributors

  • @jack-powers made their first contribution in https://github.com/EC-KitY/EC-KitY/pull/96
  • @eliadsbgu made their first contribution in https://github.com/EC-KitY/EC-KitY/pull/129

Full Changelog: https://github.com/EC-KitY/EC-KitY/compare/0.3.4...0.4.0

- Python
Published by itaitzruia4 over 1 year ago

eckity - EC-KitY 0.3.2

Several improvements:

  • Termination Checker can now also be a list. In that case, early termination is performed if any of the termination conditions apply.
  • Generations now range from 1 to maxgeneration, instead of 0 to maxgeneration - 1. Individual now contains informative fields - id, gen, clonedfrom, selectedby and applied_operators.
  • Vector and its subtypes can now receive a list of the genome (vector cells) as constructor argument.

- Python
Published by itaitzruia4 almost 3 years ago

eckity - EC-KitY 0.3.1

ProcessPoolExecutor Previously, fitness evaluation tasks could only be submitted to a ThreadPoolExecutor. From now on, the tasks can also be submitted to a ProcessPoolExecutor (enhanced performance in some cases).

When initializing an instance of SimpleEvolution, use the parameter executor=process for a ProcessPoolExecutor, or executor=thread for a ThreadPoolExecutor (the default is value is thread).

- Python
Published by itaitzruia4 about 3 years ago

eckity - EC-KitY 0.3.0

Multi-Objective Evolution

MOE - multi-objective evolution - from now on you could use EC-KitY to run an evolutionary algorithm with multiple objectives, to receive the Pareto front and find the best solution(s) for you.

- Python
Published by itaitzruia4 over 3 years ago

eckity - EC-KitY 0.2.3

IndividualEvaluator.evaluate signature change

IndividualEvaluator.evaluate now also receives the entire sub-population individuals, and not only the current individual. Useful for non-simple cases in which the individual's fitness also depends on the rest of the individuals in its sub-population.

- Python
Published by itaitzruia4 almost 4 years ago

eckity - EC-KitY 0.2.2

bug fixes for float vector and tests

- Python
Published by tomerhal almost 4 years ago

eckity - EC-KitY 0.2.1

Bugfixes to GA IntVector, FloatVector and their appropriate Creator classes

- Python
Published by itaitzruia4 almost 4 years ago

eckity - 0.2.0

EC-KitY - version 0.2.0

Support for Genetic Algorithms

This version supports: * Genetic Algorithms (GA) Vector representation * Bit Vectors, Integer Vectors and Float Vectors * Crossover operators: K Point Crossover * Mutation operators: One Point and N Point Mutation * Two GA examples: One Max, Knapsack

- Python
Published by itaitzruia4 almost 4 years ago

eckity - 0.1.1

EC-KitY - Version 0.1.1

In addition to the features described in version 0.1.0, EC-KitY now also includes Pypi support (i.e. pip-installable).

- Python
Published by itaitzruia4 about 4 years ago

eckity - 0.1.0

EC-KitY - version 0.1.0

Initial release of EC-KitY: Evolutionary Computation tool kit in Python

This version supports: * Genetic Programming (GP) tree representation * Two fundamental modes: Basic mode and sklearn mode * Creating GP trees using either Grow, Full. or Ramped-Half-and-Half * Selection methods: Tournament Selection * Elitism * Crossover operators: Subtree Crossover * Mutation operators: Subtree Mutation, ERC Mutation * Concurrent fitness evaluation * Statistics * Two basic-mode (non-sklearn) GP examples: Symbolic Regression, Multiplexer * Two sklearn-mode GP examples: Symbolic Regression, Breast Cancer * sklearn compatibility showcased through use of Pipeline and Grid search * Adding user-defined problems and fitness functions

- Python
Published by itaitzruia4 about 4 years ago