tvanderpol

tvanderpol

Genetic Algorithms in Elixir: First stab at Ch1 algorithm converges just fine? (p28)

I’ve got something of the opposite of the usual problem - my code doesn’t fail in the way the text suggests it should (when it suggests premature convergence is the cause).

I’ve ran the code a fair bit and did some debug print injecting and such but I can’t really see anything out of the ordinary - it converges on the correct answer almost instantly if I run it without any additional debug and it takes a reasonable amount of generations from what I can tell by poking at it.

Now I am clear to continue the chapter as-is, and I understand the argument being made, but I don’t understand how the algorithm is meant to fail (I do in the abstract but I mean the code as written) and that’s bugging me.

Marked As Solved

seanmor5

seanmor5

Author of Genetic Algorithms in Elixir

There was a mistake in my version of the code that forced early convergence with smaller populations that isn’t present in the book’s transcription of the code.

To better demonstrate premature convergence: set chromosome size to 1000 and population size to 100. You’ll notice your version without mutation converges much slower than with mutation. You can continue to decrease the population size further and further and you’ll reach a point where progress completely stops.

Sorry about the confusion!

Also Liked

christhekeele

christhekeele

This was on quite a new Macbook, which may be influencing results from what’s expected.

MacBook Pro (16-inch, 2019)
2.4 GHz 8-Core Intel Core i9
32 GB 2667 MHz DDR4
Erlang/OTP 22 [erts-10.7.2] [64-bit] [smp:16:16]
Elixir 1.10.3

tvanderpol

tvanderpol

Perfect, thank you for responding so fast! That helps my understanding of exactly how it fails a lot.

christhekeele

christhekeele

I noticed this in the B1 edition as well!

Re:

$ elixir one_max.exs
Current Best: 32

But wait, what’s going on here? Why is the algorithm stopping on a best fitness below 42? No matter how many times you run it, the algorithm will almost always certainly stop improving below 42. The problem is premature convergence.

At this point, the chapter’s example code looks like:

Code to Date
population = for _ <- 1..10, do: for _ <- 1..42, do: Enum.random(0..1)

evaluate = fn population ->
  Enum.sort_by(population, &Enum.sum(&1), &>=/2)
end

selection = fn population ->
  population
  |> Enum.chunk_every(2)
  |> Enum.map(&List.to_tuple(&1))
end

crossover = fn population ->
  Enum.reduce(population, [], fn {p1, p2}, acc ->
    cx_point = :rand.uniform(42)
    {{h1, t1}, {h2, t2}} = {Enum.split(p1, cx_point), Enum.split(p2, cx_point)}
    [h1 ++ t2 | [h2 ++ t1 | acc]]
  end)
end

algorithm = fn population, algorithm ->
  best = Enum.max_by(population, &Enum.sum(&1))
  IO.write("\rCurrent Best: " <> Integer.to_string(Enum.sum(best)))
  if Enum.sum(best) == 42 do
    best
  else
    population
    |> evaluate.()
    |> selection.()
    |> crossover.()
    |> algorithm.(algorithm)
  end
end

solution = algorithm.(population, algorithm)
IO.write("\n Answer is \n")
IO.inspect solution

I parameterized it thusly:

Tunable version
-population = for _ <- 1..10, do: for _ <- 1..42, do: Enum.random(0..1)
+problem_size = 42
+population_size = 100
+
+population = for _ <- 1..population_size, do: for _ <- 1..problem_size, do: Enum.random(0..1)

 evaluate = fn population ->
   Enum.sort_by(population, &Enum.sum(&1), &>=/2)
 end

 selection = fn population ->
   population
   |> Enum.chunk_every(2)
   |> Enum.map(&List.to_tuple(&1))
 end

 crossover = fn population ->
   Enum.reduce(population, [], fn {p1, p2}, acc ->
-    cx_point = :rand.uniform(42)
+    cx_point = :rand.uniform(problem_size)
     {{h1, t1}, {h2, t2}} = {Enum.split(p1, cx_point), Enum.split(p2, cx_point)}
     [h1 ++ t2 | [h2 ++ t1 | acc]]
   end)
 end

 algorithm = fn population, algorithm ->
   best = Enum.max_by(population, &Enum.sum(&1))
   IO.write("\rCurrent Best: " <> Integer.to_string(Enum.sum(best)))
-  if Enum.sum(best) == 42 do
+  if Enum.sum(best) == problem_size do
     best
   else
     population
     |> evaluate.()
     |> selection.()
     |> crossover.()
     |> algorithm.(algorithm)
   end
 end

 solution = algorithm.(population, algorithm)
 IO.write("\n Answer is \n")
 IO.inspect solution

In my experimentation, with problem_size = 42, not only did population_size = 100 always converge on the best answer, but even as low as population_size = 8 consistently converged. I started seeing the need for mutation around population_size = 6, which normally gets stuck around 35.

Alternatively, increasing the size of the problem to problem_size = 420 usually converged correctly, but with enough time to watch things work. problem_size = 4200 consistently gets stuck around 2400, as the narrative of the chapter wants it to.

Where Next?

Popular Pragmatic Bookshelf topics Top

ianwillie
Hello Brian, I have some problems with running the code in your book. I like the style of the book very much and I have learnt a lot as...
New
raul
Page 28: It implements io.ReaderAt on the store type. Sorry if it’s a dumb question but was the io.ReaderAt supposed to be io.ReadAt? ...
New
jeremyhuiskamp
Title: Web Development with Clojure, Third Edition, vB17.0 (p9) The create table guestbook syntax suggested doesn’t seem to be accepted ...
New
patoncrispy
I’m new to Rust and am using this book to learn more as well as to feed my interest in game dev. I’ve just finished the flappy dragon exa...
New
jskubick
I think I might have found a problem involving SwitchCompat, thumbTint, and trackTint. As entered, the SwitchCompat changes color to hol...
New
adamwoolhether
Is there any place where we can discuss the solutions to some of the exercises? I can figure most of them out, but am having trouble with...
New
taguniversalmachine
Hi, I am getting an error I cannot figure out on my test. I have what I think is the exact code from the book, other than I changed “us...
New
EdBorn
Title: Agile Web Development with Rails 7: (page 70) I am running windows 11 pro with rails 7.0.3 and ruby 3.1.2p20 (2022-04-12 revision...
New
Keton
When running the program in chapter 8, “Implementing Combat”, the printout Health before attack was never printed so I assumed something ...
New
redconfetti
Docker-Machine became part of the Docker Toolbox, which was deprecated in 2020, long after Docker Desktop supported Docker Engine nativel...
New

Other popular topics Top

AstonJ
poll poll Be sure to check out @Dusty’s article posted here: An Introduction to Alternative Keyboard Layouts It’s one of the best write-...
New
AstonJ
There’s a whole world of custom keycaps out there that I didn’t know existed! Check out all of our Keycaps threads here: https://forum....
New
New
AstonJ
Just done a fresh install of macOS Big Sur and on installing Erlang I am getting: asdf install erlang 23.1.2 Configure failed. checking ...
New
AstonJ
This looks like a stunning keycap set :orange_heart: A LEGENDARY KEYBOARD LIVES ON When you bought an Apple Macintosh computer in the e...
New
AstonJ
We’ve talked about his book briefly here but it is quickly becoming obsolete - so he’s decided to create a series of 7 podcasts, the firs...
New
PragmaticBookshelf
Leverage Elixir and the Nx ecosystem to build intelligent applications that solve real-world problems in computer vision, natural languag...
New
PragmaticBookshelf
25 puzzles that will make you a better C programmer by challenging your knowledge of the language, and explaining the technical details o...
New
AnfaengerAlex
Hello, I’m a beginner in Android development and I’m facing an issue with my project setup. In my build.gradle.kts file, I have the foll...
New
AstonJ
This is a very quick guide, you just need to: Download LM Studio: https://lmstudio.ai/ Click on search Type DeepSeek, then select the o...
New

Sub Categories: