107,129 research outputs found
DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm.
We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes of multi-objective evolutionary algorithms (MOEAs): classical approaches that use Pareto-based selection for survival criteria, approaches that rely on differential evolution, and decomposition-based strategies. A key part of our hybrid evolutionary approach lies in the proposed fitness sharing mechanism that is able to smoothly transfer information between the coevolved subpopulations without negatively impacting the specific evolutionary process behavior that characterizes each subpopulation. The proposed MOEA also features an adaptive allocation of fitness evaluations between the coevolved populations to increase robustness and favor the evolutionary search strategy that proves more successful for solving the MOOP at hand. Apart from the new evolutionary algorithm, this paper also contains the description of a new hypervolume and racing-based methodology aimed at providing practitioners from the field of multi-objective optimization with a simple means of analyzing/reporting the general comparative run-time performance of multi-objective optimization algorithms over large problem sets
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Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images
We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes
Multi-objective optimization based network control principles for identifying personalized drug targets with cancer
It is a big challenge to develop efficient models for identifying
personalized drug targets (PDTs) from high-dimensional personalized genomic
profile of individual patients. Recent structural network control principles
have introduced a new approach to discover PDTs by selecting an optimal set of
driver genes in personalized gene interaction network (PGIN). However, most of
current methods only focus on controlling the system through a minimum
driver-node set and ignore the existence of multiple candidate driver-node sets
for therapeutic drug target identification in PGIN. Therefore, this paper
proposed multi-objective optimization-based structural network control
principles (MONCP) by considering minimum driver nodes and maximum prior-known
drug-target information. To solve MONCP, a discrete multi-objective
optimization problem is formulated with many constrained variables, and a novel
evolutionary optimization model called LSCV-MCEA was developed by adapting a
multi-tasking framework and a rankings-based fitness function method. With
genomics data of patients with breast or lung cancer from The Cancer Genome
Atlas database, the effectiveness of LSCV-MCEA was validated. The experimental
results indicated that compared with other advanced methods, LSCV-MCEA can more
effectively identify PDTs with the highest Area Under the Curve score for
predicting clinically annotated combinatorial drugs. Meanwhile, LSCV-MCEA can
more effectively solve MONCP than other evolutionary optimization methods in
terms of algorithm convergence and diversity. Particularly, LSCV-MCEA can
efficiently detect disease signals for individual patients with BRCA cancer.
The study results show that multi-objective optimization can solve structural
network control principles effectively and offer a new perspective for
understanding tumor heterogeneity in cancer precision medicine.Comment: 15 pages, 8 figures; This work has been submitted to IEEE
Transactions on Evolutionary Computatio
ND-Tree-based update: a Fast Algorithm for the Dynamic Non-Dominance Problem
In this paper we propose a new method called ND-Tree-based update (or shortly
ND-Tree) for the dynamic non-dominance problem, i.e. the problem of online
update of a Pareto archive composed of mutually non-dominated points. It uses a
new ND-Tree data structure in which each node represents a subset of points
contained in a hyperrectangle defined by its local approximate ideal and nadir
points. By building subsets containing points located close in the objective
space and using basic properties of the local ideal and nadir points we can
efficiently avoid searching many branches in the tree. ND-Tree may be used in
multiobjective evolutionary algorithms and other multiobjective metaheuristics
to update an archive of potentially non-dominated points. We prove that the
proposed algorithm has sub-linear time complexity under mild assumptions. We
experimentally compare ND-Tree to the simple list, Quad-tree, and M-Front
methods using artificial and realistic benchmarks with up to 10 objectives and
show that with this new method substantial reduction of the number of point
comparisons and computational time can be obtained. Furthermore, we apply the
method to the non-dominated sorting problem showing that it is highly
competitive to some recently proposed algorithms dedicated to this problem.Comment: 15 pages, 21 figures, 3 table
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