Marc-André Gardner

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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, parametrizable by configuration files and usable by non-programmers such as public health specialists. SCHNAPS is a populationbased simulator, using hybrid-state agents to simulate timedriven 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)
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)
Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Markov Model (HMM) is a well-known graphical model useful for(More)
Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Markov Model (HMM) is a well-known graphical model useful for(More)
Fig. 1. Given a single LDR image of an indoor scene, our method automatically predicts HDR lighting (insets, tone-mapped for visualization). By learning a direct mapping from image appearance to scene lighting from large amounts of real image data, it requires no additional information about the scene and can even recover light sources that are not visible(More)
Genetic programming is a hyperheuristic optimization approach that seeks to evolve various forms of symbolic computer programs, in order to solve a wide range of problems. However, the approach can be severely hindered by a significant computational burden and stagnation of the evolution caused by uncontrolled code growth. This paper introduces HARM-GP, a(More)