Things I’ve learned reading just ONE paper

One of the most important parts of the research job is reading papers. As an early stages of a new research line you are going to deep, first month of reading may seem fruitless, because you can think you are not doing anything, nor developing new experiments or whatever. But, I realized, reading one of my friends’ (and another member of GeNeura) paper how many information you can learn in just one paper. As an example, I am going to write here all concepts I’ve learned reading his paper «EvAg: a scalable peer-to-peer evolutionary algorithm» (you can download it from here).

It is a paper about P2P populations where each individual of a GA is present in a different node (fine-grain) in the network and explain how to interchange information among the nodes. Many experiments are performed and all that stuff, but I am going to concentrate in some concepts, not in the paper results, because Juanlu explained them in previous posts of this blog.

Panmictic population: each member of the population has the same probability to reproduce with the others. Applying to a graph, it is a fully connected graph.

Selectorecombinative GA: Is a mutation-free GA. It is used in population studies, due to all diversity source is from the initial population.

Trap functions: scalable and decomposable functions (divided in Building Blocks, BB). Varying the number k of each block also varies the problem difficult. Fitness depends of the number of 1s in the block. For example, in a k=4. F(0) = 3, F(1)=2, F(11)=1, F(111)=0, F(1111) = 4. It could be deceptive (one of the edges leads to a sub-optimun, and other to a global optimum).

Watts-Strogatts network: It is a small-world graph where nodes forms clusters, like real Internet. It is very easy to create a WS network from a ring topology (just alter each edge to connect another node with a probability p, so some connections are deleted). In these networks, selective pressure is equivalent to panmitic populations, but more scalable due there are a lower number of edges.

Takeover time: time that the best individual take over the entire population.

Metrics for experiments:

• Success Rate: number of times algorithm finds the optimum.
• Average Evaluations to find Solution (AES)
• Best fitness convergence
• Genotipic entropy: diversity of the population based in the genotipic distance to optimum genome (using Hamming Distance, for example). Wilcoxon test can be used.

As you can see, no information about the results of this paper is presented in this post, I’ve just written about new things I didn’t knew.

Tip: I am using Evernote to take notes of every paper I read. Evernote is like Dropbox, but with notes. I annotate shops, projects, todo lists… and I have full synchronization with all my computers (in Mac OS X, GNU/Linux) and mobile devices (Android or iPhone). There exist many usages of this program, so I recommend to take a look.

Pablo.

A formal workshop on informal computation

Through the conferences we have attended, there have been several papers on what we would call informal evolutionary computation: peer to peer computing, browser-based distributed evolutionary computation, or even fluid evolutionary algorithms. This is a topic we like, but we think that it has not been sufficiently covered by special sessions or workshop, that is why we proposed a workshop on this topic and we called it International Workshop on Distributed Evolutionary Computation in Informal Environments, IWDECIE, which was accepted.
If you work on this topic, we would be delighted to hear from you and having you present a paper at this first edition of the workshop. We are requesting a (minimum) two-page extended abstract, with final papers due right before the workshop. Papers will be published in ArXiV during the conference, but after the event we will be considering them for a special issue in the Natural Computing journal.
And we don’t do spam, so you will not receive an email from us unless you subscribe to one of the mailing lists we have sent the announcement, so we ask from you to pass this announcement to those you know that could be interested in the subject (and with a personal message, if possible). Thanks!

Paper Seminar: Evolutionary Computing and Autonomic Computing: Shared Problems, Shared Solutions?

This Friday, March 11th, we will discuss the paper Evolutionary Computing and Autonomic Computing: Shared Problems, Shared Solutions? by Prof. A.E. Eiben in our Friday Paper Seminar. Quite a position paper about Self* properties in Evolutionary Algorithms and the other way around; Evolutionary Algortihms in Self* Computing.

You are welcome to attend the chat on Sala de Juntas at the ETSIIT.

The impact of store flyers on store traffic and store sales: a geo-marketing approach

The use of store flyers is an important part of promotional activities for attracting customers and stimulating more spending in order to increase store traffic and sales. It is also a source of revenue from manufacturers whose brands are featured on the flyer. Hence, the appropriate design of these flyers can lead to better store performance. Not only is the composition of store flyers considered in this study but also other factors such as local consumer characteristics, thereby incorporating a geomarketing approach.

El uso de folletos en tiendas forma parte de las actividades de promoción para atraer clientes y fomentar mayor compra con el fin de aumentar el movimiento en la tienda y las ventas. También es una fuente de ingresos de los fabricantes cuyas marcas se presentan en el folleto. Por lo tanto, el diseño apropiado de dichos folletos puede llevar a un mejor rendimiento de la tienda. No sólo se considera la composición de folletos si no también otros factores tales como las características del consumidor local que se incorpora un enfoque de geomarketing en este estudio.

Una presentación de este estudio en español:

Gijsbrechts, E., Campo, K., Goossens, T. (2003). The impact of store flyers on store traffic and store sales: a geo-marketing approach. Journal of Retailing, Vol. 79, Issue 1, pp. 1-16.