Protein Sequencing with an Adaptive Genetic Algorithm from Tandem Mass Spectrometry

In Proteomics, only the de novo peptide sequencing approach allows a partial amino acid sequence of a peptide to be found from a MS/MS spectrum. In this article a preliminary work is presented to discover a complete protein sequence from spectral data (MS and MS/MS spectra). For the moment, our approach only uses MS spectra. A Genetic Algorithm (GA) has been designed with a new evaluation function which works directly with a complete MS spectrum as input and not with a mass list like the other methods using this kind of data. Thus the mono isotopic peak extraction step which needs a human intervention is deleted. The goal of this approach is to discover the sequence of unknown proteins and to allow a better understanding of the differences between experimental proteins and proteins from databases

A Note on Node Coloring in the SINR Model

A $\xi$-coloring of a graph $G$ is a coloring of the nodes of $G$ with $\xi$ colors in such a way any two neighboring nodes have different colors. We prove that there exists a $O(\Delta \log n)$ time distributed algorithm computing a $O(\Delta)$-colroing for unit disc graphs under the signal-to-interference-plus-noise ratio (SINR)-based physical model ($\Delta$ is the maximum degree of the graph). We also show that, for a well defined constant $d$, a $d$-hop $O(\Delta)$-coloring allows us to schedule an interference free MAC protocol under the physical SINR constraints. For instance this allows us to prove that any point-to-point message passing algorithm with running time $\tau$ can be simulated in the SINR model in $O(\Delta (\log n + \tau))$ time using messages of well chosen size. All our algorithms are proved to be correct with high probability

Radio Network Distributed Algorithms in the Unknown Neighborhood Model

The paper deals with radio network distributed algorithms where nodes are not aware of their one hop neighborhood. Given an n-node graph modeling a multihop network of radio devices, we give a O(log^2 n) time distributed algorithm that computes w.h.p., a constant approximation value of the degree of each node. We also provide a O( \Delta log n + log^2 n) time distributed algorithm that computes w.h.p., a constant approximation value of the local maximum degree of each node, where the global maximum degree \Delta of the graph is not known. Using our algorithms as a plug-and-play procedure, we show that many existing distributed algorithms requiring the knowledge of to execute efficiently can be run with essentially the same time complexity by using the local maximum degree instead of . In other words, using the local maximum degree is sufficient to break the symmetry in a local and efficient manner. We illustrate this claim by investigating the complexity of some basic problems. First, we investigate the generic problem of simulating any classical message passing algorithm in the radio network model. Then, we study the fundamental edge/node coloring problem in the special case of unit disk graphs. The obtained results show that knowing the local maximum degree allows to coordinate the nodes locally and avoid interferences in radio networks

A Note on Node Coloring in the SINR Model

A $\xi$-coloring of a graph $G$ is a coloring of the nodes of $G$ with $\xi$ colors in such a way any two neighboring nodes have different colors. We prove that there exists a $O(\Delta \log n)$ time distributed algorithm computing a $O(\Delta)$-colroing for unit disc graphs under the signal-to-interference-plus-noise ratio (SINR)-based physical model ($\Delta$ is the maximum degree of the graph). We also show that, for a well defined constant $d$, a $d$-hop $O(\Delta)$-coloring allows us to schedule an interference free MAC protocol under the physical SINR constraints. For instance this allows us to prove that any point-to-point message passing algorithm with running time $\tau$ can be simulated in the SINR model in $O(\Delta (\log n + \tau))$ time using messages of well chosen size. All our algorithms are proved to be correct with high probability

Contraintes d'optimisation pour les problèmes d'arbres couvrants sous restrictions

Bien que des méthodes de filtrage basées sur les coûts existent depuis peu pour les problèmes d'arbres couvrants sous incertitudes \cite{Aron2002,Dooms2006}, elles ne permettent pas la prise en compte de. restrictions additionnelles. Nous présentons dans cet article une contrainte d'optimisation pour les problèmes d'arbres couvrants de poids optimal sous restrictions fondée sur les travaux de \cite{Sellmann2004}. Nous améliorons tout d'abord la contrainte d'optimisation proposée par \cite{Aron2002,Dooms2006} pour les problèmes d'arbres couvrants en mettant en évidence l'analogie entre recherche de cycles, recherche de ponts et calcul de remplaçants. Puis nous décrivons les problèmes posés par l'ajout de restrictions lors de l'utilisation de ce type d'algorithme pour proposer ensuite, grâce aux travaux de Sellmann, une nouvelle contrainte d'optimisation basée sur une relaxation Lagrangienne. Nous fournissons une première expérimentation de cette contrainte sur un problème d'arbre couvrant sous restriction de degré

CoBRA: A cooperative coevolutionary algorithm for bi-level optimization

International audienceThis article presents CoBRA, a new evolutionary algorithm, based on a coevolutionary scheme, to solve bi-level optimization problems. It handles population-based algorithms on each level, each one cooperating with the other to provide solutions for the overall problem. Moreover, in order to evaluate the relevance of CoBRA against more classical approaches, a new performance assessment methodology, based on rationality, is introduced. An experimental analysis is conducted on a bi-level distribution planning problem, where multiple manufacturing plants deliver items to depots, and where a distribution company controls several depots and distributes items from depots to re- tailers. The experimental results reveal significant enhancements, particularly over the lower level, with respect to a more classical approach based on a hierarchical scheme