69 research outputs found

    Reinforcement learning based local search for grouping problems: A case study on graph coloring

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    Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms

    A Tale of Two Oxidation States: Bacterial Colonization of Arsenic-Rich Environments

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    Microbial biotransformations have a major impact on contamination by toxic elements, which threatens public health in developing and industrial countries. Finding a means of preserving natural environments—including ground and surface waters—from arsenic constitutes a major challenge facing modern society. Although this metalloid is ubiquitous on Earth, thus far no bacterium thriving in arsenic-contaminated environments has been fully characterized. In-depth exploration of the genome of the ÎČ-proteobacterium Herminiimonas arsenicoxydans with regard to physiology, genetics, and proteomics, revealed that it possesses heretofore unsuspected mechanisms for coping with arsenic. Aside from multiple biochemical processes such as arsenic oxidation, reduction, and efflux, H. arsenicoxydans also exhibits positive chemotaxis and motility towards arsenic and metalloid scavenging by exopolysaccharides. These observations demonstrate the existence of a novel strategy to efficiently colonize arsenic-rich environments, which extends beyond oxidoreduction reactions. Such a microbial mechanism of detoxification, which is possibly exploitable for bioremediation applications of contaminated sites, may have played a crucial role in the occupation of ancient ecological niches on earth

    On the Discovery of Exception Rules: A Survey

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    In this paper, we present a survey of the different approaches developed for mining exception rules. Exception rules are interesting in the context of quality measures since such rules are intrinsically satisfied by few individuals in the database and many criteria relying on the number of occurrences, such as for instance the support measure, are no longer relevant. Therefore traditional measures must be coupled with other criteria. In that context, some works have proposed to use the expert's knowledge: she/he can provide the system either with constraints on the syntactic form of the rules, thus reducing the search space, or with commonsense rules that have to be refined by the data mining process. Works that rely on either of these approaches, with their particular quality evaluation are presented in this survey. Moreover, this presentation also gives ideas on how numeric criteria can be intertwined with user-centered approaches

    A Logic for Reasoning About Decision-Theoretic Projections

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    Evolution de population par descente de gradient pour la coloration de graphe

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    International audienceEvolution de population par descente de gradient pour la coloration de graph
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