New results on the genetic cryptanalysis of TEA and reduced-round versions of XTEA

Congress on Evolutionary Computation. Portland, USA, 19-23 June 2004Recently, a simple way of creating very efficient distinguishers for cryptographic primitives such as block ciphers or hash functions, was presented by the authors. Here, this cryptanalysis attack is shown to be successful when applied over reduced round versions of the block cipher XTEA. Additionally, a variant of this genetic attack is introduced and its results over TEA shown to be the most powerful published to date

Disjointly homogeneous Banach lattices and applications

This is a survey on disjointly homogeneous Banach lattices and their applicactions. Several structural properties of this class are analyzed. In addition we show how these spaces provide a natural framework for studying the compactness of powers of operators allowing for a unified treatment of well-known results.Comment: 20 pages, to appear in Proceedings Positivity VII Conference (2013

Finding efficient nonlinear functions by means of genetic programming

7th International Conference, KES 2003. Proceedings, Part I. Oxford, UK, September 3-5, 2003The design of highly nonlinear functions is relevant for a number of different applications, ranging from database hashing to message authentication. But, apart from useful, it is quite a challenging task. In this work, we propose the use of genetic programming for finding functions that optimize a particular nonlinear criteria, the avalanche effect, using only very efficient operations, so that the resulting functions are extremely efficient both in hardware and in software.Supported by the Spanish Ministerio de Ciencia y Tecnologia research project TIC2002-04498-C05-4Publicad

On the design of state-of-the-art pseudorandom number generators by means of genetic programming

Congress on Evolutionary Computation. Portland, EEUU, 19-23 June 2004The design of pseudorandom number generators by means of evolutionary computation is a classical problem. Today, it has been mostly and better accomplished by means of cellular automata and not many proposals, inside or outside this paradigm could claim to be both robust (passing all the statistical tests, including the most demanding ones) and fast, as is the case of the proposal we present here. Furthermore, for obtaining these generators, we use a radical approach, where our fitness function is not at all based in any measure of randomness, as is frequently the case in the literature, but of nonlinearity. Efficiency is assured by using only very efficient operators (both in hardware and software) and by limiting the number of terminals in the genetic programming implementation

An estimate of necessary effort in the development of software projects

International Workshop on Intelligent Technologies for Software Engineering (WITSE'04). 19th IEEE International Conference on Automated Software Engineering (Linz, Austria, September 20th - 25th, 2004)The estimated of the effort in the development of software projects has already been studied in the field of software engineering. For this purpose different ways of measurement such as Unes of code and function points, generally addressed to relate software size with project cost (effort) have been used. In this work we are presenting a research project that deals with this field, us'mg machine learning techniques to predict the software project cost. Several public set of data are used. The analysed sets of data only relate the effort invested in the development of software projects and the size of the resultant code. For this reason, we can say that the data used are poor. Despite that, the results obtained are good, because they improve the ones obtained in previous analyses. In order to get results closer to reality we should find data sets of a bigger size that take into account more variables, thus offering more possibilities to obtain solutions in a more efficient way.Publicad

Current Reflections Regarding Women’s Participation in Mexico-United States Migration: Moving Towards a Quantitative Feminization of Migration?

The purpose of this paper is to offer a statistical approach to the debate on the feminization of the MexicoUnited States migration process. For this, the flows or the “stock” of the Mexican migration of the last decade from different statistical sources available both in Mexico and in the United States are presented. The review suggests that women have historically been part of male migration, but unfortunately they have been poorly attended by scholars and when they did they were considered only as companions, relegating them to a secondary role in the migration process. However, as of the 1980s, the migration of women becomes an undeniable fact. Since then, more and more work began to be carried out from various disciplinary and theoretical perspectives that claimed the participation of Mexican women in international migration processes. This is evidenced by the analysis of the results of the recent decade, now more and more women are migrating autonomously in search of better employment opportunities and better living conditions for their families

Evolving hash functions by means of genetic programming

Proceedings of the 8th annual conference on Genetic and evolutionary computation. Seattle, Washington, USA, July 08-12, 2006The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust and fast hash functions. We use a fitness function based on a non-linearity measure, producing evolved hashes with a good degree of Avalanche Effect. Efficiency is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.This article has been financed by the Spanish founded research MCyT project OP:LINK, Ref:TIN2005-08818-C04-02.Publicad

Finding state-of-the-art non-cryptographic hashes with genetic programming

Proceding of: 9th International Conference, Reykjavik, Iceland, September 9-13, 2006.The design of non-cryptographic hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this paper, we use the Genetic Programming paradigm to evolve collision free and fast hash functions. For achieving robustness against collision we use a fitness function based on a non-linearity concept, producing evolved hashes with a good degree of Avalanche Effect. The other main issue, efficiency, is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.This article has been financed by the Spanish founded research MCyT project OP:LINK, Ref:TIN2005-08818-C04-02