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I also like empirical science epistemology, software development (I code in C++, Python and Bash, under Linux) and popular science communication.
5_ 1 1 u 1 x v x _i 0 2
I also like empirical science epistemology, software development (I code in C++, Python and Bash, under Linux), popular science communication.
5_ 1 1 u 1 x v x _i 0 2
I also like empirical science epistemology, software development (I code in C++, Python and Bash, under Linux), popular science communication and art/science interface ().
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E-mail: johann on the same domain name than this page.
I also like empirical science epistemology, software development (I code in C++, Python and Bash, under Linux), popular science communication and art/science interface (do not hesitate to contact me if you're an artist looking for a collaboration).
; S Using performance fronts for parameter setting of stochastic metaheuristics (2009). v A seminal work in the area of multi-objective parameter setting. It's also one of the first works stating that n an experimental study can be used to design parameter-free metaheuristics on a sound basis by studying U correlations between an estimated Pareto front distribution and performances.
F Surrogate assisted feature computation for continuous problems (2016). r The first method that enable the computation of problems features in a setting that is compatible with the ~ operation of search heuristics. Allows to limit the undersampling error of biased estimators, using a surrogate model. S A crucial tool to embed dynamic parameter setting within search heuristics.
; Q Adaptive Learning Search, a New Tool to Help Comprehending Metaheuristics (2007). q An attempt at forcing metaheuristics out of metaphors. This is also the first occurrence of the idea that q simulated annealing can be viewed as a population algorithm that samples the objective function directly. k Still relevant, as hybrid sampling approaches are believed to exhibit better performances nowadays.
Innovation Research:
; Pareto-Based Multiobjective AI Planning (2013). v One of the very first Pareto-optimal approach to automated AI planning. This work extends the D A E solver and o proposes a simple multi-objective benchmark with proven optimal solutions. It also outperforms the only i known metric-sensitive solver, competing on a —albeit simpler— aggregated objective problems.
J Per instance algorithm configuration of CMA-ES with limited budget (2017). r The method that won the 2017 Black Box Optimization Competition in the single objective track. The seminal j work on learning parameters-features mapping that can be embedded within solvers. Classical CMA-ES l versions were leading the competition since years and this work enabled a performances breakthrough.
? Divide-and-Evolve: the Marriage of Descartes and Darwin (2011). r This solver won the 2011 AI planning competition in the temporal track. It was the first time a stochastic p metaheuristic won the IPC and it was on its hardest problems. It's currently used in command and control e prototypes. This work with ONERA and INRIA also led to the solver that won the following IPC.
M Line formation algorithm in a swarm of reactive robots constrained by underwater environment (2015). n One the first application of swarm intelligence where it actually is a breakthrough innovation. We has f filed a patent on this joint work with the DGA, ENSTA and UBO. The work has led to a whole new b study on the use of mini-drones for mine sweeping, which may be the future of this domain.
9 Operating Room Planning with Random Surgery Times (2007). p My first glance at automated planning. This work is an interesting combination of a classical Operations p Research approach with ideas that originates from the field of stochastic metaheuristics (most notably a T heuristic to solve the pricing sub-problem of a column generation approach).
L Robust rigid registration of retinal angiograms through optimization (2006). p The application of my thesis. Back then, it was uncommon to use search heuristics in this field and high m resolutions images could not be handled because of inefficient multi-resolution algorithms. This work i showed that global optimization can outperform the windowed approaches generally used in imaging.
Softwares
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Some Free softwares I'm proud of:
ParadisEO: an open-source full-featured evolutionary computation framework which main purpose is to help you write your own stochastic optimization algorithms.
Übergeekism: An attempt at using as many as possible cool computer science stuff to produce a single image.
Lquid prompt: A full-featured & carefully designed adaptive prompt for Bash & Zsh.
Colout: Color text streams with a polished command line interface.
Clutchlog: C++ logging system which targets versatile DEBUGGING instead of service event storage.
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