Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Predicting the impact of underground engineering on the environment requires the knowledge of natural media at different scales. In particular, understanding basic phenomena controlling the properties of rocks in the presence of complex fluids necessitates a detailed atomic description of the solid/fluid/solid contacts, the subject of Part I of the present study. First, building the solid interspace between two different crystals in a non-periodic situation is achieved using the ab initio and molecular mechanics code GenMol (TM). A description of the fluid confined within the interspace is then derived from the original genetic iterative multi-species (GIMS) algorithm implemented in the same code. This approach consists of equilibrating chemical potentials, cycle after cycle and species after species, between the confined fluid and the free natural fluid. An elementary iteration for a species k consists of different steps incrementing the number N-k of particles k, with other numbers N-k remaining constant. At each step, an optimum fluid composition is obtained by a genetic process distributing the fluid particles on a grid by stochastic shots, followed in fine by a refining process. The effectiveness of the GIMS approach is demonstrated in the case study of a fluid confined between two (0 0 1) kaolinite faces, with apertures h varying between 4 and 10 A. connected to a 9-species external solution [H2O, Cl-, Na+ CO2(aq), NaCl(aq), Ca2+, Mg2+, HCO3-, H3O+] where concentrations are ranging from 55 to 10(-4) mol/L. The results show a drastic variation in the solute/solvent and cations/ions ratios in the confined fluid when aperture h is lowered to less than 1 nm. These results obtained with a very rapid convergence of the iterative algorithm combined with a very competitive genetic optimizer are validated with high precision on a free solution. This description of contacts between crystals is original and unattainable by standard crystal interface approaches. It opens the way to understanding and/or predicting phenomena between crystals in a complex natural environment, such as adhesion or repulsion investigated in the Part II of this study.
ISSN: 0022-0248

hal-00544569
https://hal.archives-ouvertes.fr/hal-00544569
DOI : 10.1016/j.jcrysgro.2010.08.009

Éditeur(s) : HAL CCSD
Résumé : J'étais à l'époque dans le laboratoire I3S de Sophia-Antipolis. Je suis dorénavant au laboratoire GRIMAAG en Guadeloupe.
We focused on the communication problem within multi-agents systems, inspirited by nature and more precisely by the ants that are a perfect example of living integrating learning (ontogenesis) to genetic inheritance (phylogenesis): the Baldwin Effect.
We worked with agents represented by classifiers systems using genetic algorithm. We introduced a new fixed knowledge based classifiers system, the simplified Pittsburgh classifier system, which we studied in a spontaneous coordination multi-agents problem. Then, we successfully allowed “homogeneous” agents to communicate their “knowledge” using distributed elitism.
Finally, “heterogeneous” agents efficiently exchanged information thanks to a minimal communication model we defined, bound to reality and extended.
Nous nous sommes intéressés à la problématique de la communication au sein des systèmes multi-agents en nous inspirant de la nature et plus particulièrement des fourmis qui sont un parfait exemple de l'intégration de l'apprentissage du vivant (ontogenèse) au capital génétique de celui-ci (phylogénie) : l'effet Baldwin.
Nous avons choisi de travailler avec des systèmes de classeurs à algorithme génétique pour représenter les agents. Nous avons introduit un nouveau type de système de classeurs à base de connaissance de taille fixe, les systèmes de classeurs de type Pittsburgh simplifié, dont nous avons étudié les propriétés dans un contexte multi-agents de coordination spontanée.
Nous avons ensuite proposé avec succès aux agents "homogènes" de communiquer pour échanger leur "connaissance" à l'aide de l'élitisme distribué.
Enfin, les agents "hétérogènes" ont échangé efficacement des informations grâce à un modèle minimal de communication que nous avons ancré dans la réalité puis étendu.
https://tel.archives-ouvertes.fr/tel-00192372

tel-00192372
https://tel.archives-ouvertes.fr/tel-00192372
https://tel.archives-ouvertes.fr/tel-00192372/document
https://tel.archives-ouvertes.fr/tel-00192372/file/These.pdf

Éditeur(s) : HAL CCSD Wiley/Blackwell
Résumé : International audience
Microsatellites are widely used in population genetics to uncover recent evolutionary events. They are typically genotyped using capillary sequencer, which capacity is usually limited to 9, at most 12 loci for each run, and which analysis is a tedious task that is performed by hand. With the rise of next-generation sequencing (NGS), a much larger number of loci and individuals are available from sequencing: for example, on a single run of a GS Junior, 28 loci from 96 individuals are sequenced with a 30X cover. We have developed an algorithm to automatically and efficiently genotype microsatellites from a collection of reads sorted by individual (e.g. specific PCR amplifications of a locus or a collection of reads that encompass a locus of interest). As the sequencing and the PCR amplification introduce artefactual insertions or deletions, the set of reads from a single microsatellite allele shows several length variants. The algorithm infers, without alignment, the true unknown allele(s) of each individual from the observed distributions of microsatellites length of all individuals. MicNeSs, a python implementation of the algorithm, can be used to genotype any microsatellite locus from any organism and has been tested on 454 pyrosequencing data of several loci from fruit flies (a model species) and red deers (a nonmodel species). Without any parallelization, it automatically genotypes 22 loci from 441 individuals in 11 hours on a standard computer. The comparison of MicNeSs inferences to the standard method shows an excellent agreement, with some differences illustrating the pros and cons of both methods.
ISSN: 1755-098X

hal-01544754
https://hal.archives-ouvertes.fr/hal-01544754
DOI : 10.1111/1755-0998.12467

Éditeur(s) : HAL CCSD Lavoisier
Résumé : National audience
Dans cette étude, nous présentons, une méthode permettant de découvrir, par le biais de variables agrégées et d’un algorithme génétique, des informations non connues a priori et utiles pour la prédiction de crues. Cette méthode se prête particulièrement bien aux données dans lesquelles des informations à propos d’un même élément sont dispersées à travers plusieurs lignes du jeu de données initial.
In this paper, we present an heuristic based approach for feature selection in the context of flood prediction. Features are complex variables that represent aggregate values. We apply a preprocessing method on data in order to elicit relevant information that could not be easily accessible initially because it is split through several lines of a dataset. A genetic algorithm is used in order to search for the features that may prove the best performances for flood prediction.
ISSN: 1633-1311

hal-01184510
https://hal.archives-ouvertes.fr/hal-01184510

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
This work deals with the problem of finding for a given industrial target the molecule conformation leading to the most efficient crystal polymorph and/or designing unknown crystal packing of new molecules. The originality of the method relies first on constraining the domain of the most probable crystals, before converging towards the predicted packing by a double genetic-refining optimization algorithm. Validation of this approach is discussed on the case study of 46 energetic crystal compounds, using the semi Ab Initio and Molecular Mechanics code GenMol™. This validation shows that all observed structures are retrieved in any circumstances with a relatively high number of good rankings.
ISSN: 0022-0248

hal-00857875
https://hal.archives-ouvertes.fr/hal-00857875
DOI : 10.1016/j.jcrysgro.2009.04.002

Éditeur(s) : HAL CCSD Lavoisier
Résumé : International audience
In this paper, we present an heuristic based approach for feature selection in the context of flood prediction. Features are complex variables that represent aggregate values. We apply a preprocessing method on data in order to elicit relevant information that could not be easily accessible initially because it is split through several lines of a dataset. A genetic algorithm is used in order to search for the features that may prove the best performances for flood prediction.
ISSN: 1633-1311

hal-00840738
https://hal.archives-ouvertes.fr/hal-00840738
DOI : 10.3166/isi.16.3.113-139

Éditeur(s) : HAL CCSD ACM New York, NY, USA
Résumé : International audience
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning litterature to assess the performances of learning methods when facing problems containing non markov situations. Through this study, we discuss on the performances of the APCS on maze type environments and also of the efficiency of an improvement of the APCS learning method inspired from the XCS : the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solutions to these maze problem. This statement is shown through a comparison of the results obtained by the XCS, XCSM and XCSMH on distinct maze problems with these obtained by the APCS.
Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation
Atlanta, GA, United States

hal-00520607
https://hal.archives-ouvertes.fr/hal-00520607
DOI : 10.1145/1388969.1389013

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper focuses on the study of the influence of a newly implemented mechanism on a Pittsburgh-like classifier system. The Adapted Pittsburgh Classifier System is a learning classifier system that uses genetic algorithms to evolve its ruleset. The new mechanism discussed is inspired from Wilson work on the eXtended Classifier System (XCS): it allows the concerned LCS to adapt its rule set when facing a new signal by modifying an existing rule. This mechanism is called covering mechanism due to the fact that the rule is covered up by a new rule which is sensitive to this new signal. Effects of this covering mechanism are first measured on the performances of APCS on well known multi-step environments: maze-type environments (Woods 101 and E2). In addition, further measures presented in this paper indicate that we could possibly rely on the behavior of this covering mechanism to automate the research of a correct cognitive capacity needed by the APCS to solve a given multistep problem.
2008 Eighth International Conference on Hybrid Intelligent Systems
Barcelone, Spain

hal-00520608
https://hal.archives-ouvertes.fr/hal-00520608
DOI : 10.1109/HIS.2008.105

Éditeur(s) : HAL CCSD springer
Résumé : International audience
This paper focuses on the study of the behavior of a genetic algorithm based classier system, the Adapted Pittsburgh Classier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non markovian situations. Through this study, we discuss on the performance of the APCS upon two mazes (Woods 101 and Maze E2) and also on the eciency of an improvement of the APCS learning method inspired from the XCS: the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solution to these maze problems. This statement is shown through a comparison between the results obtained by the XCS on two specic maze problems and those obtained by the APCS.
Lecture notes in computer science

hal-00542922
https://hal.archives-ouvertes.fr/hal-00542922
DOI : 10.1007/978-3-642-17508-4_8

Éditeur(s) : HAL CCSD
Résumé : International audience
Geographic Information Systems (GIS) help addressing geographical and environmental issues by providing information about a region or a city as a set of maps (layers), each one displaying information about a given theme like roads, vegetation, tourist spots or museums for instance. By combining different layers on a region, one can associate a given area to characteristics from their related themes. Indeed, the information from two or more layers might be merged and then transformed into a new layer as defined in map "algebra". When a theme vocabulary is organized as a taxonomy with concepts linked by is-a relationships, there are different ways to annotate an area with a concept depending on the level selected into the layer taxonomy. In this paper, we present an heuristic- based approach for an optimal merging of such layers in a GIS. Our goal is to generate new layers which sum up information from several themes in a most useful way. Two optimization criteria are considered, the average size of resulting areas and the average informative value of their resulting annotation. We demonstrate the validity of the proposed solution, firstly, on a formal example, and then, on a real world application.
Proceedings of the 4th International Conference on Advanced Geographic Information Systems, Applications, and Services
Valencia, Spain

hal-00679555
https://hal.archives-ouvertes.fr/hal-00679555

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
Defining homologous genes is important in many evolutionary studies but raises obvious issues. Some of these issues are conceptual and stem from our assumptions of how a gene evolves, others are practical, and depend on the algorithmic decisions implemented in existing software. Therefore, to make progress in the study of homology, both ontological and epistemological questions must be considered. In particular, defining homologous genes cannot be solely addressed under the classic assumptions of strong tree thinking, according to which genes evolve in a strictly tree-like fashion of vertical descent and divergence and the problems of homology detection are primarily methodological. Gene homology could also be considered under a different perspective where genes evolve as ``public goods,'' subjected to various introgressive processes. In this latter case, defining homologous genes becomes a matter of designing models suited to the actual complexity of the data and how such complexity arises, rather than trying to fit genetic data to some a priori tree-like evolutionary model, a practice that inevitably results in the loss of much information. Here we show how important aspects of the problems raised by homology detection methods can be overcome when even more fundamental roots of these problems are addressed by analyzing public goods thinking evolutionary processes through which genes have frequently originated. This kind of thinking acknowledges distinct types of homologs, characterized by distinct patterns, in phylogenetic and nonphylogenetic unrooted or multirooted networks. In addition, we define `` family resemblances'' to include genes that are related through intermediate relatives, thereby placing notions of homology in the broader context of evolutionary relationships. We conclude by presenting some payoffs of adopting such a pluralistic account of homology and family relationship, which expands the scope of evolutionary analyses beyond the traditional, yet relatively narrow focus allowed by a strong tree-thinking view on gene evolution.
ISSN: 0737-4038

hal-01544794
https://hal.archives-ouvertes.fr/hal-01544794
DOI : 10.1093/molbev/mst228