pGA( // PanmicticGA (Classical GA)
    population = population(size = 200) { booleans(size = 100) },
    fitnessFunction = { value -> value.count { it } },
 ) { 
    random = Random(seed = 42)
    elitism = 10

    before {
        println("GA STARTED, Init population: $population")
    }

    // create evolution strategy
    evolve {
        selTournament(size = 3) // select
        cxOnePoint(chance = 0.8) // crossover
        mutFlipBit(chance = 0.2, flipBitChance = 0.01) // mutate
        evaluation() // evaluate population
        stopBy(maxIteration = 50) { bestFitness == 100 } // finish GA by conditions
    }

    after {
        println("GA FINISHED, Result = $best")
    }
 }.startBlocking()

• Panmictic (Classical) Genetic Algorithm (PanmicticGA)
  • Dynamic population size
  • Panmictic elitism
• Cellular Genetic Algorithm (CellularGA)
  • VonNeumann and Moore neighborhoods
  • Synchronous and Asynchronous types
  • Multidimensional cellular population based on Toroidal Shape
  • Cellular elitism
• Distributed Genetic Algorithm (DistributedGA), include Island Genetic Algorithm
  • Support child genetic algorithms (more than subpopulations)
  • Synchronization child GAs (launching, shared statistics, shared population)
• Built-in genetic operators: selection, crossover, mutation, evaluation, migration (DistributedGA only)
• Flexible Customization
  • Typed Chromosome (individuals), built-in variants
  • Simple custom genetic operators based on low-api level
  • Full control of the evolutionary process: mutable population, mutable fitness function, stops any moment, dynamic evolution strategies
• Parallelism support for all types of GAs powered by Kotlin Coroutines:
  • Panmictic parallelism
  • Cellular parallelism
  • Distributed parallelism
• Statistics powered by Kotlin Flows
  • Built-in best, worst, mean, median, time, size stats
  • Sessions statistics
• Explicit GA Api powered by Kotlin Coroutines: start(), stop(), resume(), restart()
• Kotlin DSL style

• api — examples of using api kgal capabilities
  • controller — using interactive api (GA.start, GA.resume, GA.stop, GA.restart)
  • customization — GA customization possibilities for various tasks
  • population — creating populations for GA
  • statistics — working with statistics
  • api capabilities — great example of solving the One Max Problem using various kgal api capabilities
• tasks — a collection of examples of solved problems using kgal
  • one max problem — solving of the most famous problem for GA
  • extremum search — a collection of problems of finding extrema of complex functions
  • single source shortest paths — solution to the SSSP problem by GA
  • travelling salesman problem — solution to the TSP by GA + comparison with dynamic programming

Add dependencies (you can also add other modules that you need):

<dependency>
    <groupId>io.github.orthodoxal</groupId>
    <artifactId>kgal-core</artifactId>
    <version>0.0.5</version>
</dependency>

And make sure that you use the latest Kotlin version:

<properties>
    <kotlin.version>2.0.20</kotlin.version>
</properties>

Add dependencies (you can also add other modules that you need):

dependencies {
    implementation("io.github.orthodoxal:kgal-core:0.0.5")
}

And make sure that you use the latest Kotlin version:

plugins {
    // For build.gradle.kts (Kotlin DSL)
    kotlin("jvm") version "2.0.20"
    
    // For build.gradle (Groovy DSL)
    id "org.jetbrains.kotlin.jvm" version "2.0.20"
}

Make sure that you have mavenCentral() in the list of repositories:

repositories {
    mavenCentral()
}

KGAL fully supports Kotlin Multiplatform.

In common code that should get compiled for different platforms, you can add a dependency to kgal-core right to the commonMain source set:

commonMain {
    dependencies {
        implementation("io.github.orthodoxal:kgal-core:0.0.5")
    }
}

See documentation generated by dokka.