Distributed Estimation of Distribution Algorithms for continuous optimization: how does the exchanged information influence their behavior?

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems ? the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark.

An information reconciliation protocol for secret-key agreement with small leakage

We report on a variant of the so-called Cascade protocol that is well-known for its usage as information reconciliation protocol in quantum cryptography. A theoretical analysis of the optimal size of the parity check blocks is provided. We obtain a very small leakage which is for block sizes of 2^16 typically only 2.5% above the Shannon limit, and notably, this holds for a QBER between 1% and 50%. For a QBER between 1% and 6% the leakage is only 2% above the Shannon limit. As comparison, the leakage of the original Cascade algorithm is 20% (40%) above the Shannon limit for a QBER of 10% (35%).