cGA

cGA(
    // Set a population configuration
    // population size = 6 * 6 * 6 = 216 (cube)
    population = population(dimens = Dimens.cube(length = 6)) {
        booleans(size = 100)
    },
    // Set a fitness function
    fitnessFunction = { value -> value.count { it } },
{
    random = Random(seed = 42) // set pseudo random number generator
    elitism = true // set elitism
    cellularType = CellularType.Synchronous // set cellular type
    neighborhood = Moore(radius = 1) // set cellular neighborhood

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

    evolve {
        // Start to evolve all cells of cellular population with their neighborhoods
        // This operator perform N evolutionary strategies for each cell, where N - cells count
        evolveCells {
            selTournament(size = 3) // select neighbor as second parent
            cxOnePoint(chance = 0.8) // crossover
            mutFlipBit(chance = 0.2, flipBitChance = 0.01) // mutate child
            evaluation() // evaluate child
        }

        stopBy(maxIteration = 50) { bestFitness == 100 } // finish GA by conditions
    }

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