Marc-André Gardner

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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)
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)
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)
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)
EDITORIAL Editorial W elcome to the new issue of SIGEVOlution. More than a year has passed from the last editorial and I apologize for the huge delay. I hope you missed "us" since "we" missed you too! Like a phoenix, the mythological long-lived bird that is cyclically regenerated or reborn, here we are again with a new juicy menu. We start with an article(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)
—More and more, robotics is perceived in education as being an excellent way to promote higher quality learning among students, by grounding theoretical concepts into reality. In order to maximize the learning throughput, the focus of any robotics software platform should be on ease of use, with little time spent integrating the components together. To this(More)
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