[1]

Zainab Ali Abbood, and Franck P. Vidal. Basic, dual, adaptive, and directed mutation operators in the Fly algorithm.
In Biennial International Conference on Artificial Evolution, volume ???? of
Lecture Notes in Computer Science, pages ??????, Paris, France,
October 2017. Springer, Heidelberg.
Our work is based on a Cooperative Coevolution Algorithm  the Fly algorithm  in which individuals correspond to 3D 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, (kNdimensions, 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

[2]

Z. Ali Abbood, O. Amlal, and F. P. Vidal.
Evolutionary Art Using the Fly Algorithm.
In Applications of Evolutionary Computation, volume 10199 of
Lecture Notes in Computer Science, pages 455470, Amsterdam, The Netherlands,
April 2017. Springer, Heidelberg.
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 coevolution 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 stereovision in robotics. It has also been used in image reconstruction for tomography. Until now the individuals correspond to simplistic primitives: Infinitely small 3D 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 opensource software for image manipulation.
Keywords: Digital mosaic; Evolutionary art; Fly algorithm; Parisian evolution; Cooperative coevolution

[3]

F. P. Vidal, Y. L. Pavia, J.M. Rocchisani, J. Louchet, and É. Lutton.
Artificial evolution strategy for pet reconstruction.
In International Conference on Medical Imaging Using
BioInspired and Soft Computing (MIBISOC2013), pages 3946, Brussels,
Belgium, May 2013.
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 threedimensional (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 coevolution 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 twodimensional (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

[4]

F. P. Vidal, P.F. Villard, and É. Lutton.
Automatic tuning of respiratory model for patientbased simulation.
In International Conference on Medical Imaging Using
BioInspired and Soft Computing (MIBISOC2013), pages 225231, Brussels,
Belgium, May 2013.
This paper is an overview of a method recently published
in a biomedical journal (IEEE Transactions on Biomedical
Engineering, http://tbme.embs.org). The method is based
on an optimisation technique
called “evolutionary strategy” and it has been designed to
estimate the parameters of a complex 15D 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 realvalued
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

[5]

P.F. Villard, F. P. Vidal, F. Bello, and N. W. John.
A method to compute respiration parameters for patientbased
simulators.
In Proceeding of Medicine Meets Virtual Reality 19  NextMed
(MMVR19), volume 173 of Studies in Health Technology and Informatics,
pages 529533, Newport Beach, California, February 2012. IOS Press.
Winner of the best poster award.
We propose a method to automatically tune a patientbased virtual environment training
simulator for abdominal needle insertion. The key attributes to be customized in our framework are
the elasticity of softtissues 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.

[6]

F. P. Vidal, É. Lutton, J. Louchet, and J.M. Rocchisani.
Threshold selection, mitosis and dual mutation in cooperative
coevolution: application to medical 3D tomography.
In International Conference on Parallel Problem Solving From
Nature (PPSN'10), volume 6238 of Lecture Notes in Computer Science,
pages 414423, Krakow, Poland, September 2010. Springer, Heidelberg.
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
coevolution 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 coevolution 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 testcase.
Their extension to other cooperative coevolution schemes is discussed.

[7]

F. P. Vidal, J. Louchet, J.M. Rocchisani, and É. Lutton.
New genetic operators in the Fly algorithm: application to medical
PET image reconstruction.
In Applications of Evolutionary Computation, volume 6024 of
Lecture Notes in Computer Science, pages 292301, Istanbul, Turkey,
April 2010. Springer, Heidelberg.
Nominated for best paper award.
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 “thresholdedselection” 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.

[8]

F. P. Vidal, D. LazaroPonthus, S. Legoupil, J. Louchet, É. Lutton, and
J.M. Rocchisani.
Artificial evolution for 3D PET reconstruction.
In Proceedings of the 9th international conference on Artificial
Evolution (EA'09), volume 5975 of Lecture Notes in Computer Science,
pages 3748, Strasbourg, France, October 2009. Springer, Heidelberg.
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.
