Service Oriented Architecture for Research (an example in Evolutionary Computation)

Past week I presented my research line to other young researchers of the CITIC-UGR, inside the CITICoffee meetings (a Science Coffee to discuss about our work, without bosses or pressure, but with coffee and pastries!).

Although the slides are in Spanish, there are also diagrams with text in English, so it is not difficult to follow. They also include a Jackie Chan meme!

If you are interested in this kind of research (further results are now in the revision process), check this preliminary paper: draft or published version in Springer Link.

Testing different diversity-enhacing migration and replacement policies in dynamic environments (an evolutionary robotics case)

The paper “Testing Diversity-Enhancing Migration Policies for Hybrid On-Line Evolution of Robot Controllers” has been published in Evostar 2012. This work was developed during my foreign stay at the Vrije Universiteit Amsterdam, with Doctor A.E. Eiben. Appart from having a great time of my life in Amsterdam, I did experiments, and science and stuff.

In this work, we present the results obtained from comparing several migration policies that tries to optimize in a noisy fitness environment: the on-line, on-board and hybrid evolutionary robotics problem. Three different migration policies have been studied (the most different migrant, random migrant and best migrant) and two replacement mechanisms: the migrant replaces the worst, or the migrant replaces the worst after being evaluated only if is better. Experiments with 4, 16 and 36 robots were conduced, with two different topologies (ring and panmictic) and also a comparison with other evolutionary robotics algorithms were performed. Results show that the replacement mechanism has more influence than the migration policy or topology, and it also affects the tuning of the algorithm parameters. We asked ourselves the next questions:

  • Using the hybrid approach (island model), which is the best combination of migration policy, admission policy, and island topology?
  • Is this combination better than the encapsulated and distributed alternatives?
  • Does the number of robots affect the result and if so, how?

Conclusions, graphs and stuff and in the paper, but summarizing, multikulti technique (receive the most different individual of my population from other islands) and accept it in my population after its evaluation perform better than other alternatives, even with less migration rate.

You can also check the poster here.

The Springer link to the paper is  Testing Diversity-Enhancing Migration Policies for Hybrid On-Line Evolution of Robot Controllers but you can download the draft.

The abstract:

We investigate on-line on-board evolution of robot controllers based on the so-called hybrid approach (island-based). Inherently to this approach each robot hosts a population (island) of evolving controllers and exchanges controllers with other robots at certain times. We compare different exchange (migration) policies in order to optimize this evolutionary system and compare the best hybrid setup with the encapsulated and distributed alternatives. We conclude that adding a difference-based migrant selection scheme increases the performance.

Friday Paper Seminar: BEA (Bacterial Evolutionary Algorithm)

Today in our Friday Thursday Paper Seminar we’ve been discussing the paper “Fuzzy System Parameters Discovery by Bacterial Evolutionary Algorithm” of Norberto Eiji Nawa and Takeshi Furuhashi.

It’s a very interesting paper because introduces a bioinspired algorithm based in bacteria (as the title says, obviously), and its application to fuzzy system. The main improvement from the previous algorithm of the authors the Pseudo-Bacterial Evolutionary Algorithm is the fact of using the transduction, the way that the bacterias interchanges information between themselves. But let’s begin from the beginning.

Every individual of the population is a bacteria whose chromosome is the parameters of a fuzzy system. This chromosome has low epistasis, that is, the chromosoma have weak interrelationships between parts so it is possible to perform optimization in parts. So in each generation every bacteria have a local improvement called bacterial mutation, cloning the bacteria and improving every part but taking care of the global fitness, so the best clone is chosen.

The second operator presented is the Gene Transfer Operation. Instead crossover, the best bacterias sends information to the worst bacterias adding or overwriting parts of the chromosome.

After explain the algorithm the authors performs several experiments to compare other algorithms and this one. I’m not expert in Fuzzy Systems, so probably I’ll be wrong, but I can see that the while the BEA has lower error rates evaluating train set, it also has higher error rates in test set, and the fuzzy system obtained has more (and not used) fuzzy rules. I think this is not a good improvement, but the idea of bacterial mutation and gene transfer is quite interesting in other problems, as we can see in more papers that refers this one.

So, that’s all! Until next week!

Pablo (aka Fergu)