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‎README.md‎

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--------
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## Projects, papers, journals, books, tutorials and apps using GeneticSharp
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## Projects, papers, journals, books, tutorials, courses and apps using GeneticSharp
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* [AeroVision: aircraft trajectories optimization and visualization (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/AeroVision-Aircraft-trajectories-optimization-and-visualization.pdf)
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* [Analysis and comparison between Black-Scholes and Merton and Corrado-Su for options pricing (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Analysis-and-comparison-between-Black-Scholes-and-Merton-and-Corrado-Su-for-options-pricing.pdf)
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* [Artificial Intelligence course in French engineering schools (course)](https://github.com/giacomelli/GeneticSharp/pull/43#issuecomment-433662175)
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* [Analysis and comparison between Black-Scholes and Merton and Corrado-Su for options pricing (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Analysis-and-comparison-between-Black-Scholes-and-Merton-and-Corrado-Su-for-options-pricing.pdf) 🇧🇷
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* [Context-Sensitive Code Completion: improving predictions with genetic algorithms (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Context-Sensitive-Code-Completion-improving-predictions-with-genetic-algorithms.pdf) [(Github)](https://github.com/godtopus/GeneCSCC)
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* [Deriving Functions for Pareto Optimal Fronts Using Genetic Programming (paper/book)](https://books.google.com.br/books?id=w_lcDwAAQBAJ&lpg=PA473&ots=Rv7O6FhCSM&dq=%22GeneticSharp%22&hl=pt-BR&pg=PA464#v=onepage&q=%22GeneticSharp%22&f=false)
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* [Designing and creating a self managing distributed file system (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Designing-and-creating-a-self-managing-distributed-file-system.pdf)
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* [Developing trading strategies with genetic algorithms (forum)](https://www.quantconnect.com/forum/discussion/2396/developing-trading-strategies-with-genetic-algorithms) [(paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Developing-Trading-Strategies-with-Genetic-Algorithms.pdf)
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* [Function optimization with GeneticSharp (tutorial)](http://diegogiacomelli.com.br/function-optimization-with-geneticsharp/)
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* [GeneticSharp Car2D (sample)](http://diegogiacomelli.com.br/GeneticSharp-Car2D/)
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* [GeneticSharp Unity3d Samples (Android app)](https://play.google.com/store/apps/details?id=br.com.diegogiacomelli.geneticsharprunnersunityapp)
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* [Genetic Scheduler: a genetic algorithm for scheduling tasks with temporal restriction in distributed systems (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Genetic-Scheduler.pdf)
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* [Genetic Scheduler: a genetic algorithm for scheduling tasks with temporal restriction in distributed systems (paper)](https://github.com/giacomelli/GeneticSharp/blob/master/docs/mentioning-GeneticSharp/Genetic-Scheduler.pdf) 🇧🇷
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* [Lean Optimization: genetic optimization using LEAN (GitHub)](https://github.com/Jay-Jay-D/LeanOptimization)
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* [Overload journal 142: Evolutionary computing frameworks for optimisation (journal)](https://accu.org/var/uploads/journals/Overload142.pdf)
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* [Path Finding with Genetic Algorithms (project)](https://yoloprogramming.com/post/2017/01/11/path-finding-with-genetic-algorithms)
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### [GTK# sample](src/GeneticSharp.Runner.GtkApp)
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#### TSP (Travelling Salesman Problem) and Function optimization
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#### TSP (Travelling Salesman Problem), Function optimization and Sudoku
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![](docs/gifs/GeneticSharp-GtkApp.gif)
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#### Bitmap equality

‎appveyor.yml‎

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version: 2.0.{build}
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version: 2.2.{build}
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os: Visual Studio 2017
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configuration: Release
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environment:
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- cmd: >-
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mkdir .\src\nuget
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dotnet pack src/GeneticSharp.Domain/GeneticSharp.Domain.csproj -c release --no-build --output ../nuget /p:PackageVersion=2.1.0
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dotnet pack src/GeneticSharp.Domain/GeneticSharp.Domain.csproj -c release --no-build --output ../nuget /p:PackageVersion=2.2.0
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dotnet pack src/GeneticSharp.Extensions/GeneticSharp.Extensions.csproj -c release --no-build --output ../nuget /p:PackageVersion=2.1.0
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dotnet pack src/GeneticSharp.Extensions/GeneticSharp.Extensions.csproj -c release --no-build --output ../nuget /p:PackageVersion=2.2.0
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test_script:
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- cmd: dotnet clean src/GeneticSharp.Domain.UnitTests

‎src/GeneticSharp.Domain/GeneticAlgorithm.cs‎

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using System;
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using System.Collections.Generic;
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using System.Diagnostics;
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using System.Linq;
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using GeneticSharp.Domain.Chromosomes;
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using GeneticSharp.Domain.Crossovers;
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private bool m_stopRequested;
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private readonly object m_lock = new object();
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private GeneticAlgorithmState m_state;
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private Stopwatch m_stopwatch;
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#endregion
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#region Constructors
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if (shouldStop)
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{
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var handler = Stopped;
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if (handler != null)
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{
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handler(this, EventArgs.Empty);
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}
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Stopped?.Invoke(this, EventArgs.Empty);
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}
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}
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}
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lock (m_lock)
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{
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State = GeneticAlgorithmState.Started;
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var startDateTime = DateTime.Now;
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m_stopwatch = Stopwatch.StartNew();
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Population.CreateInitialGeneration();
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TimeEvolving = DateTime.Now - startDateTime;
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m_stopwatch.Stop();
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TimeEvolving = m_stopwatch.Elapsed;
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}
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Resume();
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}
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bool terminationConditionReached = false;
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DateTime startDateTime;
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do
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{
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break;
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}
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startDateTime = DateTime.Now;
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m_stopwatch.Restart();
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terminationConditionReached = EvolveOneGeneration();
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TimeEvolving += DateTime.Now - startDateTime;
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m_stopwatch.Stop();
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TimeEvolving += m_stopwatch.Elapsed;
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}
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while (!terminationConditionReached);
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}
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Population.EndCurrentGeneration();
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var handler = GenerationRan;
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if (handler != null)
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{
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handler(this, EventArgs.Empty);
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}
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handler?.Invoke(this, EventArgs.Empty);
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if (Termination.HasReached(this))
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{
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State = GeneticAlgorithmState.TerminationReached;
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handler = TerminationReached;
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if (handler != null)
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{
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handler(this, EventArgs.Empty);
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}
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handler?.Invoke(this, EventArgs.Empty);
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return true;
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}

‎src/GeneticSharp.Domain/Properties/SolutionInfo.cs‎

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[assembly: AssemblyConfiguration("Release")]
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#endif
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[assembly: AssemblyVersion("2.1.0")]
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[assembly: AssemblyFileVersion("2.1.0.0")]
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[assembly: AssemblyVersion("2.2.0")]
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[assembly: AssemblyFileVersion("2.2.0.0")]

‎src/GeneticSharp.Extensions.UnitTests/GeneticSharp.Extensions.UnitTests.csproj‎

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<PackageReference Include="NUnit3TestAdapter" Version="3.9.0" />
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<PackageReference Include="Microsoft.NET.Test.Sdk" Version="15.5.0" />
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</ItemGroup>
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<ItemGroup>
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<None Update="Sudoku\SudokuList.sdk">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</None>
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</ItemGroup>
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</Project>

‎src/GeneticSharp.Extensions.UnitTests/Multiple/MultipleTest.cs‎

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using GeneticSharp.Domain;
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using GeneticSharp.Domain.Chromosomes;
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using GeneticSharp.Domain.Crossovers;
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using GeneticSharp.Domain.Fitnesses;
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using GeneticSharp.Domain.Mutations;
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using GeneticSharp.Domain.Populations;
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using GeneticSharp.Domain.Selections;
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using GeneticSharp.Domain.Terminations;
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using GeneticSharp.Extensions.Multiple;
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using GeneticSharp.Extensions.Tsp;
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using NUnit.Framework;
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namespace GeneticSharp.Extensions.UnitTests.Multiple
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{
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[TestFixture]
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[Category("Extensions")]
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class MultipleTest
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{
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[Test()]
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public void Evolve_CompareToSingleChromosome_Evolved()
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{
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int numberOfCities = 30;
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var selection = new EliteSelection();
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var crossover = new UniformCrossover();
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var mutation = new TworsMutation();
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// Given enough generations, the Multiple Chromosome should start exhibiting convergence
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// we compare TSP /25 gen with 3*TSP / 500 gen
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IChromosome chromosome = new TspChromosome(numberOfCities);
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IFitness fitness = new TspFitness(numberOfCities, 0, 1000, 0, 1000);
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var population = new Population(30, 30, chromosome);
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var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
42+
{
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Termination = new GenerationNumberTermination(26)
44+
};
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ga.Start();
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var simpleChromosomeDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
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chromosome = new MultipleChromosome((i) => new TspChromosome(numberOfCities), 3);
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//MultiChromosome should create 3 TSP chromosomes and store them in the corresponding property
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Assert.AreEqual(((MultipleChromosome)chromosome).Chromosomes.Count, 3);
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var tempMultiFitness = ((MultipleChromosome)chromosome).Chromosomes.Sum(c => fitness.Evaluate(c));
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fitness = new MultipleFitness(fitness);
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//Multi fitness should sum over the fitnesses of individual chromosomes
54+
Assert.AreEqual(tempMultiFitness, fitness.Evaluate(chromosome));
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population = new Population(30, 30, chromosome);
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ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
57+
{
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Termination = new GenerationNumberTermination(501)
59+
};
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ga.Start();
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var bestTSPChromosome = (TspChromosome)((MultipleChromosome)ga.Population.BestChromosome).Chromosomes
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.OrderByDescending(c => c.Fitness).First();
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var multiChromosomesDistance = bestTSPChromosome.Distance;
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Assert.Less(multiChromosomesDistance, simpleChromosomeDistance);
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}
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}
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}

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