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Segmentation of Microscopic Images with NSGA-II
Computación y Sistemas, 2018This paper addresses the problem of multiobjective segmentation on microscopic images by using the evolutionary algorithm NSGA-II. Two objective functions are used at the optimization process: Otsu’s inter-class variance and Shannon’s entropy. A set of 71 images of blood cells are used.
Rocio Ochoa-Montiel +3 more
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Journal of Electromagnetic Waves and Applications, 2008
The multi-objective optimization NSGA-II approach has been proved that, in most of times, it has much better spread of solutions and better convergence near the true Pareto-optimal than mostly Pareto-optimal method. But there are also disadvantages to restrict the spread uniformity in some problems. This paper overcomes these disadvantages to get three
J. OuYang +3 more
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The multi-objective optimization NSGA-II approach has been proved that, in most of times, it has much better spread of solutions and better convergence near the true Pareto-optimal than mostly Pareto-optimal method. But there are also disadvantages to restrict the spread uniformity in some problems. This paper overcomes these disadvantages to get three
J. OuYang +3 more
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Multi-objective classification based on NSGA-II
International Journal of Computing Science and Mathematics, 2018Yu Xue, Tinghuai Ma, Jingfa Liu
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NSGA-II with local perturbation
2017 29th Chinese Control And Decision Conference (CCDC), 2017To improve the overall performance of one algorithm, most researchers focus on the fitness assignment, preserving diversity and hybridizing different search methods. Different from the above strategies, this paper focuses on the convergence. According to the analysis of the convergence of NSGA-II, local perturbation strategy is introduced to improve ...
Maoqing Zhang +3 more
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Optimizing risk management using NSGA-II
2012 IEEE Congress on Evolutionary Computation, 2012Companies are often susceptible to uncertainties which can disturb the achievement of their objectives. The effect of these uncertainties can be perceived as risk that will be taken. A healthful company have to anticipate undesired events by defining a process for managing risks.
Marcos Álvares Barbosa Junior +2 more
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Revisiting the NSGA-II crowding-distance computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation, 2013This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's ...
Félix-Antoine Fortin, Marc Parizeau
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The effect of offspring population size on NSGA-II
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021Non-Dominated Sorting Genetic Algorithm (NSGA-II) is one of the most popular Multi-Objective Evolutionary Algorithms (MOEA) and has been applied to a large range of problems. Previous studies have shown that parameter tuning can improve NSGA-II performance.
Max Hort, Federica Sarro
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Fast implementation of steady-state NSGA-II
2016 IEEE Congress on Evolutionary Computation (CEC), 2016In steady-state evolutionary algorithms, the parent population is updated each time once a new offspring solution is generated. Due to the updation of the parent population, the non-dominated sorting needs to be applied again and again. The repetition of non-dominated sorting makes steady-state algorithms computationally expensive. But the recent study
Sumit Mishra +2 more
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Optimization of LDO voltage regulators by NSGA-II
2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2016Two different low-dropout (LDO) voltage regulators are optimized by applying the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). First, from a sensitivity analysis a set of design variables are selected to establish a reduced chromosome for performing multi-objective optimization by NSGA-II.
Jesus Lopez-Arredondo +2 more
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