paralleleureqa.jl

include("eureqa.jl")

println("Lets try to learn (x2^2 + cos(x3)) using regularized evolution from scratch")
const nthreads = Threads.nthreads()
println("Running with $nthreads threads")
const npop = 300
const annealing = true
const ncyclesperiteration = 3000

function fullRun(niterations::Integer)
    # Generate random initial populations
    allPops = [Population(npop, 3) for j=1:nthreads]
    # Repeat this many evolutions; we collect and migrate the best
    # each time.
    for k=1:niterations

        # Spawn threads to run indepdent evolutions, then gather them
        @inbounds Threads.@threads for i=1:nthreads
            allPops[i] = run(allPops[i], ncyclesperiteration, annealing, verbose=500)
        end

        # Get best 10 models from each evolution. Copy because we re-assign later.
        bestPops = deepcopy(Population([member for pop in allPops for member in bestSubPop(pop).members]))
        bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
        bestCurScore = bestPops.members[bestCurScoreIdx].score
        println(bestCurScore, " is the score for ", stringTree(bestPops.members[bestCurScoreIdx].tree))

        # Migration
        for j=1:nthreads
            for k in rand(1:npop, Integer(npop/2))
                # Copy in case one gets used twice
                allPops[j].members[k] = deepcopy(bestPops.members[rand(1:size(bestPops.members)[1])])
            end
        end
    end
end

fullRun(10)