-- MarI/O by SethBling
-- Feel free to use this code, but please do not redistribute it.
-- Intended for use with the BizHawk emulator and Super Mario World.
BasePath= "C:\\Users\\Henrik\\Desktop\\"
Filename = "YI1.state"
ButtonNames = {
    "A",
    "B",
    "X",
    "Y",
    "Up",
    "Down",
    "Left",
    "Right",
}

BoxRadius = 6
TileMapInputSize = (BoxRadius*2+1)*(BoxRadius*2+1)
TotalInputSize=TileMapInputSize + 6

Inputs = TotalInputSize+1
Outputs = #ButtonNames

Population = 300
DeltaDisjoint = 2.0
DeltaWeights = 0.4
DeltaThreshold = 1.0

StaleSpecies = 15

MutateConnectionsChance = 0.25
PerturbChance = 0.90
CrossoverChance = 0.75
LinkMutationChance = 2.0
NodeMutationChance = 0.50
BiasMutationChance = 0.40
StepSize = 0.1
DisableMutationChance = 0.4
EnableMutationChance = 0.2

TimeoutConstant = 20

MaxNodes = 1000000

function getPositions()
    marioX = memory.read_s16_le(0x94)
    marioY = memory.read_s16_le(0x96)

    local layer1x = memory.read_s16_le(0x1A);
    local layer1y = memory.read_s16_le(0x1C);

    screenX = marioX-layer1x
    screenY = marioY-layer1y
end

function getTile(dx, dy)
    x = math.floor((marioX+dx+8)/16)
    y = math.floor((marioY+dy)/16)

    return memory.readbyte(0x1C800 + math.floor(x/0x10)*0x1B0 + y*0x10 + x%0x10)

end

function getSprites()
    local sprites = {}
    for slot=0,11 do
        local status = memory.readbyte(0x14C8+slot)
        if status ~= 0 then
            spritex = memory.readbyte(0xE4+slot) + memory.readbyte(0x14E0+slot)*256
            spritey = memory.readbyte(0xD8+slot) + memory.readbyte(0x14D4+slot)*256
            sprites[#sprites+1] = {["x"]=spritex, ["y"]=spritey, ["status"]=status}
        end
    end

    return sprites
end

function getExtendedSprites()
    local extended = {}
    for slot=0,11 do
        local number = memory.readbyte(0x170B+slot)
        if number ~= 0 then
            spritex = memory.readbyte(0x171F+slot) + memory.readbyte(0x1733+slot)*256
            spritey = memory.readbyte(0x1715+slot) + memory.readbyte(0x1729+slot)*256
            extended[#extended+1] = {["x"]=spritex, ["y"]=spritey,["number"]=number}
        end
    end

    return extended
end

function getInputs()
    getPositions()

    sprites = getSprites()
    extended = getExtendedSprites()

    local inputs = {}

    for dy=-BoxRadius*16,BoxRadius*16,16 do
        for dx=-BoxRadius*16,BoxRadius*16,16 do
            inputs[#inputs+1] = 0

            tile = getTile(dx, dy)
            if tile == 1 and marioY+dy < 0x1B0 then
                inputs[#inputs] = tile
            end

            for i = 1,#sprites do
                distx = math.abs(sprites[i]["x"] - (marioX+dx))
                disty = math.abs(sprites[i]["y"] - (marioY+dy))
                if distx <= 8 and disty <= 8 and sprites[i]["status"] then
                    inputs[#inputs] = -sprites[i]["status"]
                end
            end

            for i = 1,#extended do
                distx = math.abs(extended[i]["x"] - (marioX+dx))
                disty = math.abs(extended[i]["y"] - (marioY+dy))
                if distx < 8 and disty < 8 and extended[i]["number"] then
                    inputs[#inputs] = -extended[i]["number"]
                end
            end
        end
    end

    mariovx = memory.read_s8(0x7B)
    mariovy = memory.read_s8(0x7D)
    pmeter= memory.read_u8(0x13E4)
    takeoffmeter= memory.read_u8(0x149F)
    powerup=memory.read_u8(0x19)
    balloontimer=memory.read_u8(0x1891)

    inputs[TileMapInputSize+1] = mariovx
    inputs[TileMapInputSize+2] = mariovy
    inputs[TileMapInputSize+3] = pmeter
    inputs[TileMapInputSize+4] = takeoffmeter
    inputs[TileMapInputSize+5] = powerup
    inputs[TileMapInputSize+6] = balloontimer

    return inputs
end

function sigmoid(x)
    return 2/(1+math.exp(-4.9*x))-1
end

function newInnovation()
    pool.innovation = pool.innovation + 1
    return pool.innovation
end

function newPool()
    local pool = {}
    pool.species = {}
    pool.generation = 0
    pool.innovation = Outputs
    pool.currentSpecies = 1
    pool.currentGenome = 1
    pool.currentFrame = 0
    pool.maxFitness = 0

    return pool
end

function newSpecies()
    local species = {}
    species.topFitness = 0
    species.staleness = 0
    species.genomes = {}
    species.averageFitness = 0

    return species
end

function newGenome()
    local genome = {}
    genome.genes = {}
    genome.fitness = 0
    genome.adjustedFitness = 0
    genome.network = {}
    genome.maxneuron = 0
    genome.globalRank = 0
    genome.mutationRates = {}
    genome.mutationRates["connections"] = MutateConnectionsChance
    genome.mutationRates["link"] = LinkMutationChance
    genome.mutationRates["bias"] = BiasMutationChance
    genome.mutationRates["node"] = NodeMutationChance
    genome.mutationRates["enable"] = EnableMutationChance
    genome.mutationRates["disable"] = DisableMutationChance
    genome.mutationRates["step"] = StepSize

    return genome
end

function copyGenome(genome)
    local genome2 = newGenome()
    for g=1,#genome.genes do
        table.insert(genome2.genes, copyGene(genome.genes[g]))
    end
    genome2.maxneuron = genome.maxneuron
    genome2.mutationRates["connections"] = genome.mutationRates["connections"]
    genome2.mutationRates["link"] = genome.mutationRates["link"]
    genome2.mutationRates["bias"] = genome.mutationRates["bias"]
    genome2.mutationRates["node"] = genome.mutationRates["node"]
    genome2.mutationRates["enable"] = genome.mutationRates["enable"]
    genome2.mutationRates["disable"] = genome.mutationRates["disable"]

    return genome2
end

function basicGenome()
    local genome = newGenome()
    local innovation = 1

    genome.maxneuron = Inputs
    mutate(genome)

    return genome
end

function newGene()
    local gene = {}
    gene.into = 0
    gene.out = 0
    gene.weight = 0.0
    gene.enabled = true
    gene.innovation = 0

    return gene
end

function copyGene(gene)
    local gene2 = newGene()
    gene2.into = gene.into
    gene2.out = gene.out
    gene2.weight = gene.weight
    gene2.enabled = gene.enabled
    gene2.innovation = gene.innovation

    return gene2
end

function newNeuron()
    local neuron = {}
    neuron.incoming = {}
    neuron.value = 0.0

    return neuron
end

function generateNetwork(genome)
    local network = {}
    network.neurons = {}

    for i=1,Inputs do
        network.neurons[i] = newNeuron()
    end

    for o=1,Outputs do
        network.neurons[MaxNodes+o] = newNeuron()
    end

    table.sort(genome.genes, function (a,b)
        return (a.out <b> 0 then
            neuron.value = sigmoid(sum)
        end
    end

    local outputs = {}
    for o=1,Outputs do
        local button = "P1 " .. ButtonNames[o]
        if network.neurons[MaxNodes+o].value > 0 then
            outputs[button] = true
        else
            outputs[button] = false
        end
    end

    return outputs
end

function crossover(g1, g2)
    -- Make sure g1 is the higher fitness genome
    if g2.fitness > g1.fitness then
        tempg = g1
        g1 = g2
        g2 = tempg
    end

    local child = newGenome()

    local innovations2 = {}
    for i=1,#g2.genes do
        local gene = g2.genes[i]
        innovations2[gene.innovation] = gene
    end

    for i=1,#g1.genes do
        local gene1 = g1.genes[i]
        local gene2 = innovations2[gene1.innovation]
        if gene2 ~= nil and math.random(2) == 1 and gene2.enabled then
            table.insert(child.genes, copyGene(gene2))
        else
            table.insert(child.genes, copyGene(gene1))
        end
    end

    child.maxneuron = math.max(g1.maxneuron,g2.maxneuron)

    for mutation,rate in pairs(g1.mutationRates) do
        child.mutationRates[mutation] = rate
    end

    return child
end

function randomNeuron(genes, nonInput)
    local neurons = {}
    if not nonInput then
        for i=1,Inputs do
            neurons[i] = true
        end
    end
    for o=1,Outputs do
        neurons[MaxNodes+o] = true
    end
    for i=1,#genes do
        if (not nonInput) or genes[i].into > Inputs then
            neurons[genes[i].into] = true
        end
        if (not nonInput) or genes[i].out > Inputs then
            neurons[genes[i].out] = true
        end
    end

    local count = 0
    for _,_ in pairs(neurons) do
        count = count + 1
    end
    local n = math.random(1, count)

    for k,v in pairs(neurons) do
        n = n-1
        if n == 0 then
            return k
        end
    end

    return 0
end

function containsLink(genes, link)
    for i=1,#genes do
        local gene = genes[i]
        if gene.into == link.into and gene.out == link.out then
            return true
        end
    end
end

function pointMutate(genome)
    local step = genome.mutationRates["step"]

    for i=1,#genome.genes do
        local gene = genome.genes[i]
        if math.random() < PerturbChance then
            gene.weight = gene.weight + math.random() * step*2 - step
        else
            gene.weight = math.random()*4-2
        end
    end
end

function linkMutate(genome, forceBias)
    local neuron1 = randomNeuron(genome.genes, false)
    local neuron2 = randomNeuron(genome.genes, true)

    local newLink = newGene()
    if neuron1 <= Inputs and neuron2 <= Inputs then
        --Both input nodes
        return
    end
    if neuron2 <= Inputs then
        -- Swap output and input
        local temp = neuron1
        neuron1 = neuron2
        neuron2 = temp
    end

    newLink.into = neuron1
    newLink.out = neuron2
    if forceBias then
        newLink.into = Inputs
    end

    if containsLink(genome.genes, newLink) then
        return
    end
    newLink.innovation = newInnovation()
    newLink.weight = math.random()*4-2

    table.insert(genome.genes, newLink)
end

function nodeMutate(genome)
    if #genome.genes == 0 then
        return
    end

    genome.maxneuron = genome.maxneuron + 1

    local gene = genome.genes[math.random(1,#genome.genes)]
    if not gene.enabled then
        return
    end
    gene.enabled = false

    local gene1 = copyGene(gene)
    gene1.out = genome.maxneuron
    gene1.weight = 1.0
    gene1.innovation = newInnovation()
    gene1.enabled = true
    table.insert(genome.genes, gene1)

    local gene2 = copyGene(gene)
    gene2.into = genome.maxneuron
    gene2.innovation = newInnovation()
    gene2.enabled = true
    table.insert(genome.genes, gene2)
end

function enableDisableMutate(genome, enable)
    local candidates = {}
    for _,gene in pairs(genome.genes) do
        if gene.enabled == not enable then
            table.insert(candidates, gene)
        end
    end

    if #candidates == 0 then
        return
    end

    local gene = candidates[math.random(1,#candidates)]
    gene.enabled = not gene.enabled
end

function mutate(genome)
    for mutation,rate in pairs(genome.mutationRates) do
        if math.random(1,2) == 1 then
            genome.mutationRates[mutation] = 0.95*rate
        else
            genome.mutationRates[mutation] = 1.05263*rate
        end
    end

    if math.random() <genome> 0 do
        if math.random() <p> 0 do
        if math.random() <p> 0 do
        if math.random() <p> 0 do
        if math.random() <p> 0 do
        if math.random() < p then
            enableDisableMutate(genome, false)
        end
        p = p - 1
    end
end

function disjoint(genes1, genes2)
    local i1 = {}
    for i = 1,#genes1 do
        local gene = genes1[i]
        i1[gene.innovation] = true
    end

    local i2 = {}
    for i = 1,#genes2 do
        local gene = genes2[i]
        i2[gene.innovation] = true
    end

    local disjointGenes = 0
    for i = 1,#genes1 do
        local gene = genes1[i]
        if not i2[gene.innovation] then
            disjointGenes = disjointGenes+1
        end
    end

    for i = 1,#genes2 do
        local gene = genes2[i]
        if not i1[gene.innovation] then
            disjointGenes = disjointGenes+1
        end
    end

    local n = math.max(#genes1, #genes2)

    return disjointGenes / n
end

function weights(genes1, genes2)
    local i2 = {}
    for i = 1,#genes2 do
        local gene = genes2[i]
        i2[gene.innovation] = gene
    end

    local sum = 0
    local coincident = 0
    for i = 1,#genes1 do
        local gene = genes1[i]
        if i2[gene.innovation] ~= nil then
            local gene2 = i2[gene.innovation]
            sum = sum + math.abs(gene.weight - gene2.weight)
            coincident = coincident + 1
        end
    end

    return sum / coincident
end

function sameSpecies(genome1, genome2)
    local dd = DeltaDisjoint*disjoint(genome1.genes, genome2.genes)
    local dw = DeltaWeights*weights(genome1.genes, genome2.genes)
    return dd + dw < DeltaThreshold
end

function rankGlobally()
    local global = {}
    for s = 1,#pool.species do
        local species = pool.species[s]
        for g = 1,#species.genomes do
            table.insert(global, species.genomes[g])
        end
    end
    table.sort(global, function (a,b)
        return (a.fitness <b> b.fitness)
        end)

        local remaining = math.ceil(#species.genomes/2)
        if cutToOne then
            remaining = 1
        end
        while #species.genomes > remaining do
            table.remove(species.genomes)
        end
    end
end

function breedChild(species)
    local child = {}
    if math.random() <CrossoverChance> b.fitness)
        end)

        if species.genomes[1].fitness > species.topFitness then
            species.topFitness = species.genomes[1].fitness
            species.staleness = 0
        else
            species.staleness = species.staleness + 1
        end
        if species.staleness <StaleSpecies>= pool.maxFitness then
            table.insert(survived, species)
        end
    end

    pool.species = survived
end

function removeWeakSpecies()
    local survived = {}

    local sum = totalAverageFitness()
    for s = 1,#pool.species do
        local species = pool.species[s]
        breed = math.floor(species.averageFitness / sum * Population)
        if breed >= 1 then
            table.insert(survived, species)
        end
    end

    pool.species = survived
end


function addToSpecies(child)
    local foundSpecies = false
    for s=1,#pool.species do
        local species = pool.species[s]
        if not foundSpecies and sameSpecies(child, species.genomes[1]) then
            table.insert(species.genomes, child)
            foundSpecies = true
        end
    end

    if not foundSpecies then
        local childSpecies = newSpecies()
        table.insert(childSpecies.genomes, child)
        table.insert(pool.species, childSpecies)
    end
end

function newGeneration()
    cullSpecies(false) -- Cull the bottom half of each species
    rankGlobally()
    removeStaleSpecies()
    rankGlobally()
    for s = 1,#pool.species do
        local species = pool.species[s]
        calculateAverageFitness(species)
    end
    removeWeakSpecies()
    local sum = totalAverageFitness()
    local children = {}
    for s = 1,#pool.species do
        local species = pool.species[s]
        breed = math.floor(species.averageFitness / sum * Population) - 1
        for i=1,breed do
            table.insert(children, breedChild(species))
        end
    end
    cullSpecies(true) -- Cull all but the top member of each species
    while #children + #pool.species <Population> #pool.species[pool.currentSpecies].genomes then
        pool.currentGenome = 1
        pool.currentSpecies = pool.currentSpecies+1
        if pool.currentSpecies > #pool.species then
            newGeneration()
            pool.currentSpecies = 1
        end
    end
end

function fitnessAlreadyMeasured()
    local species = pool.species[pool.currentSpecies]
    local genome = species.genomes[pool.currentGenome]

    return genome.fitness ~= 0
end

function displayGenome(genome)
    local network = genome.network
    local cells = {}
    local i = 1
    local cell = {}
    for dy=-BoxRadius,BoxRadius do
        for dx=-BoxRadius,BoxRadius do
            cell = {}
            cell.x = 50+5*dx
            cell.y = 70+5*dy
            cell.value = network.neurons[i].value
            cells[i] = cell
            i = i + 1
        end
    end
    local biasCell = {}
    biasCell.x = 80
    biasCell.y = 110
    biasCell.value = network.neurons[Inputs].value
    cells[Inputs] = biasCell

    for o = 1,Outputs do
        cell = {}
        cell.x = 220
        cell.y = 30 + 8 * o
        cell.value = network.neurons[MaxNodes + o].value
        cells[MaxNodes+o] = cell
        local color
        if cell.value > 0 then
            color = 0xFF0000FF
        else
            color = 0xFF000000
        end
        gui.drawText(223, 24+8*o, ButtonNames[o], color, 9)
    end

    for n,neuron in pairs(network.neurons) do
        cell = {}
        if n > Inputs and n <MaxNodes> Inputs and gene.into <MaxNodes>= c2.x then
                        c1.x = c1.x - 40
                    end
                    if c1.x <90> 220 then
                        c1.x = 220
                    end
                    c1.y = 0.75*c1.y + 0.25*c2.y

                end
                if gene.out > Inputs and gene.out <MaxNodes>= c2.x then
                        c2.x = c2.x + 40
                    end
                    if c2.x <90> 220 then
                        c2.x = 220
                    end
                    c2.y = 0.25*c1.y + 0.75*c2.y
                end
            end
        end
    end ]]--

    gui.drawBox(50-BoxRadius*5-3,70-BoxRadius*5-3,50+BoxRadius*5+2,70+BoxRadius*5+2,0xFF000000, 0x80808080)
    for n,cell in pairs(cells) do
        if n > Inputs or cell.value ~= 0 then
            local color = math.floor((cell.value+1)/2*256)
            if color > 255 then color = 255 end
            if color <0> 0 then
                    color = opacity + 0x8000 + 0x10000*color
                else
                    color = opacity + 0x800000 + 0x100*color
                end
                gui.drawLine(c1.x+1, c1.y, c2.x-3, c2.y, color)
            end
        end
    end

    gui.drawBox(49,71,51,78,0x00000000,0x80FF0000)

    if forms.ischecked(showMutationRates) then
        local pos = 100
        for mutation,rate in pairs(genome.mutationRates) do
            gui.drawText(100, pos, mutation .. ": " .. rate, 0xFF000000, 10)
            pos = pos + 8
        end
    end
end

function writeFile(filename)
        local file = io.open(BasePath .. filename, "w")
    file:write(pool.generation .. "\n")
    file:write(pool.maxFitness .. "\n")
    file:write(#pool.species .. "\n")
        for n,species in pairs(pool.species) do
        file:write(species.topFitness .. "\n")
        file:write(species.staleness .. "\n")
        file:write(#species.genomes .. "\n")
        for m,genome in pairs(species.genomes) do
            file:write(genome.fitness .. "\n")
            file:write(genome.maxneuron .. "\n")
            for mutation,rate in pairs(genome.mutationRates) do
                file:write(mutation .. "\n")
                file:write(rate .. "\n")
            end
            file:write("done\n")

            file:write(#genome.genes .. "\n")
            for l,gene in pairs(genome.genes) do
                file:write(gene.into .. " ")
                file:write(gene.out .. " ")
                file:write(gene.weight .. " ")
                file:write(gene.innovation .. " ")
                if(gene.enabled) then
                    file:write("1\n")
                else
                    file:write("0\n")
                end
            end
        end
        end
        file:close()
end

function savePool()
    local filename = forms.gettext(saveLoadFile)
    writeFile(filename)
end

function loadFile(filename)
        local file = io.open(BasePath .. filename, "r")
    pool = newPool()
    pool.generation = file:read("*number")
    pool.maxFitness = file:read("*number")
    forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
        local numSpecies = file:read("*number")
        for s=1,numSpecies do
        local species = newSpecies()
        table.insert(pool.species, species)
        species.topFitness = file:read("*number")
        species.staleness = file:read("*number")
        local numGenomes = file:read("*number")
        for g=1,numGenomes do
            local genome = newGenome()
            table.insert(species.genomes, genome)
            genome.fitness = file:read("*number")
            genome.maxneuron = file:read("*number")
            local line = file:read("*line")
            while line ~= "done" do
                genome.mutationRates[line] = file:read("*number")
                line = file:read("*line")
            end
            local numGenes = file:read("*number")
            for n=1,numGenes do
                local gene = newGene()
                table.insert(genome.genes, gene)
                local enabled
                gene.into, gene.out, gene.weight, gene.innovation, enabled = file:read("*number", "*number", "*number", "*number", "*number")
                if enabled == 0 then
                    gene.enabled = false
                else
                    gene.enabled = true
                end

            end
        end
    end
        file:close()

    while fitnessAlreadyMeasured() do
        nextGenome()
    end
    initializeRun()
    pool.currentFrame = pool.currentFrame + 1
end

function loadPool()
    local filename = forms.gettext(saveLoadFile)
    loadFile(filename)
end

function playTop()
    local maxfitness = 0
    local maxs, maxg
    for s,species in pairs(pool.species) do
        for g,genome in pairs(species.genomes) do
            if genome.fitness > maxfitness then
                maxfitness = genome.fitness
                maxs = s
                maxg = g
            end
        end
    end

    pool.currentSpecies = maxs
    pool.currentGenome = maxg
    pool.maxFitness = maxfitness
    forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
    initializeRun()
    pool.currentFrame = pool.currentFrame + 1
    return
end

function onExit()
    forms.destroy(form)
end

writeFile("temp.pool")

event.onexit(onExit)

form = forms.newform(200, 260, "Fitness")
maxFitnessLabel = forms.label(form, "Max Fitness: " .. math.floor(pool.maxFitness), 5, 8)
showNetwork = forms.checkbox(form, "Show Map", 5, 30)
showMutationRates = forms.checkbox(form, "Show M-Rates", 5, 52)
restartButton = forms.button(form, "Restart", initializePool, 5, 77)
saveButton = forms.button(form, "Save", savePool, 5, 102)
loadButton = forms.button(form, "Load", loadPool, 80, 102)
saveLoadFile = forms.textbox(form, Filename .. ".pool", 170, 25, nil, 5, 148)
saveLoadLabel = forms.label(form, "Save/Load:", 5, 129)
playTopButton = forms.button(form, "Play Top", playTop, 5, 170)
hideBanner = forms.checkbox(form, "Hide Banner", 5, 190)


while true do
    local backgroundColor = 0xD0FFFFFF
    if not forms.ischecked(hideBanner) then
        gui.drawBox(0, 0, 300, 26, backgroundColor, backgroundColor)
    end

    local species = pool.species[pool.currentSpecies]
    local genome = species.genomes[pool.currentGenome]

    if forms.ischecked(showNetwork) then
        displayGenome(genome)
    end

    if pool.currentFrame%5 == 0 then
        evaluateCurrent()
    end

    joypad.set(controller)

    getPositions()
    if marioX > rightmost then
        rightmost = marioX
        timeout = TimeoutConstant
    end

    timeout = timeout - 1


    local timeoutBonus = pool.currentFrame / 4
    if timeout + timeoutBonus <0> 4816 then
            fitness = fitness + 1000
        end
        if fitness == 0 then
            fitness = -1
        end
        genome.fitness = fitness

        if fitness > pool.maxFitness then
            pool.maxFitness = fitness
            forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
            writeFile("backup." .. pool.generation .. "." .. forms.gettext(saveLoadFile))
        end

        console.writeline("Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " fitness: " .. fitness)
        pool.currentSpecies = 1
        pool.currentGenome = 1
        while fitnessAlreadyMeasured() do
            nextGenome()
        end
        initializeRun()
    end

    local measured = 0
    local total = 0
    for _,species in pairs(pool.species) do
        for _,genome in pairs(species.genomes) do
            total = total + 1
            if genome.fitness ~= 0 then
                measured = measured + 1
            end
        end
    end
    if not forms.ischecked(hideBanner) then
        gui.drawText(0, 0, "Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " (" .. math.floor(measured/total*100) .. "%)", 0xFF000000, 11)
        gui.drawText(0, 12, "Fitness: " .. math.floor(rightmost - (pool.currentFrame) / 2 - (timeout + timeoutBonus)*2/3), 0xFF000000, 11)
        gui.drawText(100, 12, "Max Fitness: " .. math.floor(pool.maxFitness), 0xFF000000, 11)
    end

    pool.currentFrame = pool.currentFrame + 1

    emu.frameadvance();
end