international_conferences-peer_reviewed_articles.bib
@inproceedings{AliAbbood2017EA,
booktitle = {Biennial International Conference on Artificial Evolution (EA-2017)},
editor = {????},
title = {Basic, Dual, Adaptive, and Directed Mutation Operators in the {Fly} Algorithm},
author = {Zainab Ali Abbood and Franck P. Vidal},
year = {2017},
series = {Lecture Notes in Computer Science},
volume = {???},
pages = {??--??},
publisher = {Springer, Heidelberg},
isbn = {????},
doi = {???},
month = oct,
address = {Paris, France},
annotation = {Oct~25--27, 2017},
abstract = {Our work is based on a Cooperative Co-evolution Algorithm -- the Fly algorithm --
in which individuals correspond to 3-D points. The Fly algorithm uses two levels of fitness
function: i) a local fitness computed to evaluate a given individual (usually during
the selection process) and ii) a global fitness to assess the performance of the population
as a whole. This global fitness is the metrics that is minimised (or maximised depending on
the problem) by the optimiser. Here the solution of the optimisation problem corresponds
to a set of individuals instead of a single individual (the best individual) as in
classical evolutionary algorithms. The Fly algorithm heavily relies on mutation operators
and a new blood operator to insure diversity in the population. To lead to accurate results,
a large mutation variance is often initially used to avoid local minima (or maxima). It is
then progressively reduced to refine the results. Another approach is the use of adaptive
operators. However, very little research on adaptive operators in Fly algorithm has been
conducted. We address this deficiency and propose 4 different fully adaptive mutation
operators in the Fly algorithm: positrons, and the final solution of the algorithm
approximates the radioactivity concentration. The view and analysis four mutation operators,
which are Basic Mutation, Adaptive Mutation Variance, Dual Mutation, and Directed Mutation.
Due to the complex nature of the search space, ($kN$-dimensions, with $k$ the number of
genes per individuals and $N$ the number of individuals in the population),
we favour operators with a low maintenance cost in terms of computations.
Their impact on the algorithm efficiency is analysed and validated on positron emission
tomography (PET) reconstruction.},
keywords = {evolutionary algorithms, Parisian approach, reconstruction algorithms, positron emission tomography, mutation operator}
}
@inproceedings{Abbood2017EvoIASP,
author = {Z. {Ali Abbood} and O. Amlal and F. P. Vidal},
title = {Evolutionary Art Using the Fly Algorithm},
booktitle = {Applications of Evolutionary Computation},
year = 2017,
series = {Lecture Notes in Computer Science},
volume = 10199,
pages = {455-470},
month = apr,
address = {Amsterdam, The Netherlands},
annotation = {Apr~19--21, 2017},
abstract = {This study is about Evolutionary art such as digital mosaics.
The most common techniques to generate a digital mosaic effect heavily
rely on Centroidal Voronoi diagrams. Our method generates artistic images
as an optimisation problem without the introduction of any a priori
knowledge or constraint other than the input image. We adapt
a cooperative co-evolution strategy based on the Parisian evolution
approach, the Fly algorithm, to produce artistic visual effects from
an input image (e.g. a photograph). The primary usage of the Fly algorithm
is in computer vision, especially stereo-vision in robotics. It has also
been used in image reconstruction for tomography. Until now the individuals
correspond to simplistic primitives: Infinitely small 3-D points. In this
paper, the individuals have a much more complex representation and
represent tiles in a mosaic. They have their own position, size, colour,
and rotation angle. We take advantage of graphics processing units (GPUs)
to generate the images using the modern OpenGL Shading Language. Different
types of tiles are implemented, some with transparency, to generate
different visual effects, such as digital mosaic and spray paint. A user
study has been conducted to evaluate some of our results. We also compare
results with those obtained with GIMP, an open-source software for image
manipulation.},
doi = {10.1007/978-3-319-55849-3_30},
publisher = {Springer, Heidelberg},
keywords = {Digital mosaic, Evolutionary art, Fly algorithm,
Parisian evolution, Cooperative co-evolution},
pdf = {pdf/Abbood2017EvoIASP.pdf},
}
@inproceedings{Vidal2013MIBISOC-A,
author = {F. P. Vidal and Y. L. Pavia and {J.-M.} Rocchisani and
J. Louchet and \'E. Lutton},
title = {Artificial Evolution Strategy for PET Reconstruction},
booktitle = {Proceeding of the International Conference on Medical Imaging
Using Bio-inspired and Soft Computing (MIBISOC2013)},
year = 2013,
month = may,
address = {Brussels, Belgium},
annotation = {May~15--17, 2013},
note = {To appear},
abstract = {This paper shows new resutls of our artificial evolution algorithm
for positron emission tomography (PET) reconstruction. This imaging
technique produces datasets corresponding to the concentration of positron
emitters within the patient. Fully three-dimensional (3D) tomographic
reconstruction requires high computing power and leads to many challenges.
Our aim is to produce high quality datasets in a time that is clinically
acceptable. Our method is based on a co-evolution strategy called the ``Fly
algorithm''. Each fly represents a point in space and mimics a positron
emitter. Each fly position is progressively optimised using evolutionary
computing to closely match the data measured by the imaging system. The
performance of each fly is assessed based on its positive or negative
contribution to the performance of the whole population. The final
population of flies approximates the radioactivity concentration. This
approach has shown promising results on numerical phantom models. The size
of objects and their relative concentrations can be calculated in
two-dimensional (2D) space. In 3D, complex shapes can be reconstructed. In
this paper, we demonstrate the ability of the algorithm to fidely
reconstruct more anatomically realistic volumes.},
keywords = {Evolutionary computation, inverse problems, adaptive algorithm,
Nuclear medicine, Positron emission tomography, Reconstruction algorithms}
}
@inproceedings{Vidal2013MIBISOC-B,
author = {F. P. Vidal and {P.-F.} Villard and \'E. Lutton},
title = {Automatic tuning of respiratory model for patient-based simulation},
booktitle = {Proceeding of the International Conference on Medical Imaging
Using Bio-inspired and Soft Computing (MIBISOC2013)},
year = 2013,
month = may,
address = {Brussels, Belgium},
annotation = {May~15--17, 2013},
note = {To appear},
abstract = {This paper is an overview of a method recently published in a biomedical journal (IEEE Transactions on Biomedical Engineering).
The method is based on an optimisation technique called ``evolutionary strategy'' and it has been designed to estimate the parameters of a complex 15-D respiration model.
This model is adaptable to account for patient's specificities.
The aim of the optimisation algorithm is to finely tune the model so that it accurately fits real patient datasets.
The final results can then be embedded, for example, in high fidelity simulations of the human physiology.
Our algorithm is fully automatic and adaptive.
A compound fitness function has been designed to take into account for various quantities that have to be minimised (here topological errors of the liver and the diaphragm geometries).
The performance our implementation is compared with two traditional methods (downhill simplex and conjugate gradient descent), a random search and a basic real-valued genetic algorithm.
It shows that our evolutionary scheme provides results that are significantly more stable and accurate than the other tested methods.
The approach is relatively generic and can be easily adapted to other complex parametrisation problems when ground truth data is available.},
keywords = {Evolutionary computation, inverse problems, medical simulation,
adaptive algorithm}
}
@inproceedings{Villard2012MMVR,
author = {{P.-F.} Villard and F. P. Vidal and F. Bello and N. W. John},
title = {A Method to Compute Respiration Parameters for Patient-based
Simulators},
booktitle = {Proceeding of Medicine Meets Virtual Reality 19 - NextMed
(MMVR19)},
year = 2012,
series = {Studies in Health Technology and Informatics},
volume = 173,
pages = {529-533},
month = feb,
address = {Newport Beach, California},
annotation = {Feb~9--11, 2012},
note = {Winner of the best poster award},
abstract = {We propose a method to automatically tune a patient-based virtual
environment training simulator for abdominal needle insertion. The key
attributes to be customized in our framework are the elasticity of
soft-tissues and the respiratory model parameters. The estimation is based
on two 3D Computed Tomography (CT) scans of the same patient at two
different time steps. Results are presented on five patients and show that
our new method leads to better results than our previous studies with
manually tuned parameters.},
pmid = {22357051},
publisher = {IOS Press}
}
@inproceedings{Vidal2010PPSN,
author = {F. P. Vidal and \'E. Lutton and J. Louchet and {J.-M.} Rocchisani},
title = {Threshold selection, mitosis and dual mutation in cooperative
coevolution: application to medical {3D} tomography},
booktitle = {International Conference on Parallel Problem Solving From Nature
(PPSN'10)},
year = 2010,
series = {Lecture Notes in Computer Science},
volume = 6238,
pages = {414-423},
month = sep,
address = {Krakow, Poland},
annotation = {Sept~11--15, 2010},
abstract = {We present and analyse the behaviour of specialised
operators designed for cooperative coevolution strategy in the framework of
3D tomographic PET reconstruction. The basis is a simple cooperative
co-evolution scheme (the ``fly algorithm''), which embeds the searched
solution in the whole population, letting each individual be only a part of
the solution. An individual, or fly, is a 3D point that emits positrons.
Using a cooperative co-evolution scheme to optimize the position of
positrons, the population of flies evolves so that the data estimated from
flies matches measured data. The final population approximates the
radioactivity concentration. In this paper, three operators are proposed,
threshold selection, mitosis and dual mutation, and their impact on the
algorithm efficiency is experimentally analysed on a controlled test-case.
Their extension to other cooperative co-evolution schemes is discussed.},
doi = {10.1007/978-3-642-15844-5_42},
publisher = {Springer, Heidelberg}
}
@inproceedings{Vidal2010EvoIASP,
author = {F. P. Vidal and J. Louchet and {J.-M.} Rocchisani and \'E. Lutton},
title = {New genetic operators in the {Fly} algorithm: application to medical
{PET} image reconstruction},
booktitle = {Applications of Evolutionary Computation},
year = 2010,
series = {Lecture Notes in Computer Science},
volume = 6024,
pages = {292-301},
month = apr,
address = {Istanbul, Turkey},
annotation = {Apr~7--9, 2010},
note = {Nominated for best paper award},
abstract = {This paper presents an evolutionary approach for image
reconstruction in positron emission tomography (PET). Our reconstruction
method is based on a cooperative coevolution strategy (also called Parisian
evolution): the ``fly algorithm''. Each fly is a 3D point that mimics a
positron emitter. The flies' position is progressively optimised using
evolutionary computing to closely match the data measured by the imaging
system. The performance of each fly is assessed using a ``marginal
evaluation'' based on the positive or negative contribution of this fly to
the performance of the population. Using this property, we propose a
``thresholded-selection'' method to replace the classical tournament method.
A mitosis operator is also proposed. It is triggered to automatically
increase the population size when the number of flies with negative fitness
becomes too low.},
doi = {10.1007/978-3-642-12239-2_30},
publisher = {Springer, Heidelberg}
}
@inproceedings{Vidal2009EA,
author = {F. P. Vidal and D. {Lazaro-Ponthus} and S. Legoupil and
J. Louchet and \'E. Lutton and {J.-M.} Rocchisani},
title = {Artificial Evolution for {3D} {PET} Reconstruction},
booktitle = {Proceedings of the 9th international conference on Artificial
Evolution (EA'09)},
year = 2009,
series = {Lecture Notes in Computer Science},
volume = 5975,
pages = {37-48},
month = oct,
address = {Strasbourg, France},
annotation = {OCt~26--28, 2009},
abstract = {This paper presents a method to take advantage of artificial
evolution in positron emission tomography reconstruction. This imaging
technique produces datasets that correspond to the concentration of positron
emitters through the patient. Fully 3D tomographic reconstruction requires
high computing power and leads to many challenges. Our aim is to reduce the
computing cost and produce datasets while retaining the required quality.
Our method is based on a coevolution strategy (also called Parisian
evolution) named ``Fly algorithm''. Each fly represents a point of the space
and acts as a positron emitter. The final population of flies corresponds to
the reconstructed data. Using ``marginal evaluation'', the fly's fitness is
the positive or negative contribution of this fly to the performance of the
population. This is also used to skip the relatively costly step of
selection and simplify the evolutionary algorithm.},
doi = {10.1007/978-3-642-14156-0_4},
publisher = {Springer, Heidelberg}
}
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