Programming

A year ago, I mentioned that I always write a cGA implementation when I learn a new language. Then, I was trying to get back to fluent in Haskell. A couple of days ago, Martin Pelikan just did the same and wanted to compare implementations. So, what did I do? I looked for my implementation to post it here. I took a look at the code and change a couple of things, but I can say that the Haskell implementation is the shortest working implementation that I have ever written in any language. It is shorter than the versions I wrote in Scala and Erlang. Python could get awkwardly compressed using some functional flavor to get close to this, but dynamic typing… C, C++, Java, Go and other friends, are far away when you look in the rear Haskell mirror. Anyway, the code below implements cGA for binary strings. You chose the population size, the number of bits, and the evaluation function. Also, some of the constructs are simple and elegant that do not need much explanation (not to mention maintainability…) ...

Back in Fall 2006 I was lucky to be at the right place, at the right time. Kumara Sastry and David E. Goldberg were working to pulverize some preconceptions about how far you could scale genetic algorithms. As I said, I was lucky I could help the best I could. It turned out that the answer was pretty simple, as far as you want. The key to that result was, again, built on Georges Harik’s compact genetic algorithm. The results were published on a paper titled Toward routine billion-variable optimization using genetic algorithms if you are curious. Anyway, back on track. A few days ago, I was playing with Erlang and I coded, just for fun, [yet another cGA implementation, now in Erlang]({{ ref “/posts/yet-another-cga-implementation-now-in-erlang.md” >}} yet another cGA implementation, now in Erlang"). The code was pretty straight forward, so why not take another crack at it and write an Erlang version that uses some of the ideas we used on that paper. The idea we used on the paper was simple. Slice the probabilistic model into smaller segments and update all those model fragments in parallel. The only caveat, if you go over the cGA model, is that you need the evaluation of two individuals to decide which way to update the model. Also, you need to know when to stop, or when your global model has converged. The flow is pretty simple: ...

Wanna have some Sunday afternoon fun? Just refresh your Erlang skills. Since this is me having fun, what better way to do so than to write yet another implementation of the compact Genetic Algorithm originally (cGA) proposed by Georges Harik? I am going to skip describing the original algorithm and focus a bit on how to implement it in Erlang instead. You can find some nice books elsewhere and more information on the Erlang site. Erlang is an interesting mix of functional and logic programming languages. If you ever wrote code in ProLog, Erlang is going to look familiar. It will also look familiar if you are coming from Haskell, although, being Erlang a dynamically typed language, you will miss the type system and inference. Nevertheless, give it a chance. It concurrent model is worthwhile reading about. I will it for further posts thought. ...

A lot of water under the bridge has gone by since the first release of Meandre 1.4.X series. In January I went back to the drawing board and start sketching what was going to be 1.5.X series. The slide deck embedded above is a extended list of the thoughts during the process. As usual, I started collecting feedback from people using 1.4.X in production, things that worked, things that needed improvement, things that were just plain over complicated. The hot recurrent topics that people using 1.4.X could be mainly summarized as: ...

“On the Road to Competence” is a slide deck by Jurgen Appelo with interesting analogies between learning classifier systems and software development. Definitely worth taking a look at it.