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DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://deap.gel.ulaval.ca,(More)
DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates(More)
In this paper, we present SCHNAPS, a generic simulator designed for health care modelling and simulations, parametriz-able by configuration files and usable by non-programmers such as public health specialists. SCHNAPS is a population-based simulator, using hybrid-state agents to simulate time-driven models. Its software architecture integrates some(More)
Recent bloat control methods such as dynamic depth limit (DynLimit) and Dynamic Operator Equalization (DynOpEq) aim at modifying the tree size distribution in a population of genetic programs. Although they are quite efficient for that purpose, these techniques have the disadvantage of evaluating the fitness of many bloated Genetic Programming (GP) trees,(More)
We present a high-fidelity monitoring infrastructure that enables real-time analysis and self-adaptation at both the systems and applications level in virtual computing environments. We believe that such an infrastructure is needed as each paradigm shift (in this case to virtual computing environments) brings new challenges along with new capabilities.(More)
As Beowulf clusters have grown in size and complexity, the task of monitoring the performance, status, and health of such clusters has become increasingly more difficult but also more important. Consequently, tools such as Ganglia and Supermon have emerged in recent years to provide the robust support needed for scalable cluster monitoring. However, the(More)
In this work, we propose a method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. Inferring scene illumination from a single photograph is a challenging problem. The pixel intensities observed in a photograph are a complex function of scene geometry, reflectance properties, and(More)