All Versions
10
Latest Version
Avg Release Cycle
28 days
Latest Release
1751 days ago

Changelog History

  • v2.6.0 Changes

    July 11, 2019

    Blazor template

    Now we have 4 dotnet new templates, besides the GeneticSharpConsoleApp, GeneticSharpTspConsoleApp and GeneticSharpTspUnity3d already existent, a new template for a Blazor client app was added: GeneticSharpTspBlazorApp:

    dotnet new -i GeneticSharp.Templates
dotnet new GeneticSharpTspBlazorApp -o TspBlazorApp
cd TspBlazorApp
dotnet run

    If you want to know more about how to use GeneticSharp with Blazor, take a look in this tutorial TSP with GeneticSharp and Blazor.

    🆕 New papers and projects using GeneticSharp

    Four papers and one project were added to the list:

    How to install the new version

    .NET Standard 2.0

    Only GeneticSharp:

    install-package GeneticSharp

    🏗 GeneticSharp and extensions (TSP, AutoConfig, Bitmap equality, Equality equation, Equation solver, Function builder, etc):

    install-package GeneticSharp.Extensions

    Unity3D

    You should use the UnityNuGet to install GeneticSharp directly from NuGet.

    🚀 Or you can use the latest GeneticSharp.unitypackage available on our release page.

    Let's evolve!

  • v2.5.2 Changes

    June 12, 2019

    🐛 Bug fix

    • AlternatingPositionCrossover throw IndexOutOfRangeException #61

    ⚡️ NuGet update

    Only GeneticSharp:

    update-package GeneticSharp

    🏗 GeneticSharp and extensions (TSP, AutoConfig, Bitmap equality, Equality equation, Equation solver, Function builder, etc):

    update-package GeneticSharp.Extensions

  • v2.5.1 Changes

    January 31, 2019

    🐛 Bug fix

    • 👻 Runtime exception on .NET 4.6.2 #58

    ⚡️ NuGet update

    Only GeneticSharp:

    update-package GeneticSharp

    🏗 GeneticSharp and extensions (TSP, AutoConfig, Bitmap equality, Equality equation, Equation solver, Function builder, etc):

    update-package GeneticSharp.Extensions

    Unity3d

    🚀 The bug #58 does not affect the Unity3d package, so you can still use the 2.4.0 version.

  • v2.5.0

    January 24, 2019

  • v2.4.0 Changes

    January 19, 2019

    The additions of this version are the two new crossovers implementations and a new option of ITaskExecutor that use TPL.

    Crossovers

    Alternating-position (AP)

    The alternating position crossover operator (Larrañaga et al. 1996a) simply creates an offspring by selecting alternately the next element of the first parent and the next element of the second parent, omitting the elements already present in the offspring.

    Voting Recombination Crossover (VR)

    👀 It can be seen as a P-sexual crossover operator, where p (parents number) is a natural number greater than, or equal to, 2.

    It starts by defining a threshold, which is a natural number smaller than, or equal to p.

    Next, for every; i E {l, 2, . . .N} the set of ith elements of all the parents is considered. If in this set an element occurs at least the threshold number of times, it is copied into the offspring.

    TPL

    Three new classes were implemented to run some key points of a genetic algorithm using TPL.

    Those new classes can be used alone, but normally you will use all them together. You can see a sample usage at unit test Start_TplManyGenerations_Optimization.

    TplTaskExecutor:

    An ITaskExecutor's implementation that executes the tasks in a parallel fashion using Task Parallel Library (TPL).

    TplPopulation

    Represents a population of candidate solutions (chromosomes) using TPL to create them.

    TplOperatorsStrategy

    0️⃣ A new interface called IOperatorsStrategy was added to GeneticAlgorithm as an option. Two options of operators strategy were created, the default one, called DefaultOperatorsStrategy and the new one called TplOperatosStrategy.

    Thanks to

    I would like to thanks to EMostafaAli and Alexey I. for opened some issues and made small pull requests and Dan for contributing with the TPL implementations.

    Let's evolve!

  • v2.2.3

    January 14, 2019

  • v2.2.1

    November 03, 2018

  • v2.2.0 Changes

    November 03, 2018

    The additions of this version are the new whole sample and extensions showing how to use GeneticSharp to solve a Sudoku.

    The GeneticSharp.Extensions project receive those new features:

    Multiple

    MultipleChromosome

    Compound chromosome to artificially increase genetics diversity by evolving a list of chromosomes instead of just one.
    Sub-genes are inlined into a single compound list of genes.

    MultipleFitness

    Fitness class that can evaluate a compound chromosome by summing over the evaluation of its sub-chromosomes.

    Sudoku

    ISudokuChromosome:

    Represents each type of chromosome for solving a sudoku is simply required to output a list of candidate sudokus.

    SudokuBoard

    A class that represents a Sudoku, fully or partially completed.
    Holds a list of 81 int for cells, with 0 for empty cells.
    📜 Can parse strings and files from most common formats and displays the sudoku in an easy to read format.

    SudokuCellsChromosome

    This simple chromosome simply represents each cell by a gene with a value between 1 and 9, accounting for the target mask if given.

    SudokuFitness

    Evaluates a sudoku chromosome for completion by counting duplicates in rows, columns, boxes, and differences from the target mask.

    SudokuPermutationsChromosome

    This more elaborated chromosome manipulates rows instead of cells, and each of its 9 gene holds an integer for the index of the row's permutation amongst all that respect the target mask.
    Permutations are computed once when a new Sudoku is encountered, and stored in a static dictionary for further reference.

    SudokuRandomPermutationsChromosome

    This chromosome aims at increasing genetic diversity of SudokuPermutationsChromosome, which exhibits only 9 permutation genes.
    Here, instead, an arbitrary number of Sudokus are generated where for each row, a random gene is picked amongst an arbitrary number of corresponding permutation genes.

    Samples

    GTK# sample
    gtk-sudoku-sample

    Thanks to

    I would like to thanks to Jean-Sylvain Boige (@jsboige) for contributing with those great new samples and extensions and for use GeneticSharp in his Artificial Intelligence course in French engineering schools (course).

    Take a look on the pull-request for fore details about those new features: New Sudoku extension and GTK# sample #43.

    Let's evolve!

  • v2.1.2

    October 31, 2018

  • v2.1.1

    October 31, 2018