Results 211 to 220 of about 4,635,823 (252)
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Speeding Up the NSGA-II With a Simple Tie-Breaking Rule
AAAI Conference on Artificial IntelligenceThe non-dominated sorting genetic algorithm II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcomings in discrete search spaces, namely, that the NSGA-II has difficulties ...
Benjamin Doerr +2 more
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Multi-criteria website optimisation using NSGA-II
International Journal of Business Information Systems, 2016It has been observed that organisations in order to be competitive are trying to maintain long-term relationships with their online customers. These organisations are mining information in log files or on-line forms with the aim of creating websites that can adapt to changing online buying patterns of customers.
T.V. Vijay Kumar, Kumar Dilip
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High Performance Architecture for NSGA-II
2013NSGA-II is one of the most popular algorithms for solving Multiobjective Optimization Problems. It has been used to solve different real-world optimization problems. However, NSGA-II has been criticized for its high computational cost and bad performance on applications with more than two objective functions.
Josué Domínguez +3 more
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Optimization of Planetary Gearbox Using NSGA-II
2021In this study, optimization of planetary gearbox considering regular mechanical and critical tribological constraints such as wear and scuffing is done. The design variables considered are the number of teeth in the sun, planet, ring gear, module, face width, diameter of shafts, planet pin diameter, the hardness of gear material and kinematic viscosity
Abhishek Parmar, P. Ramkumar, K. Shankar
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The asynchronous island model and NSGA-II
Proceedings of the 15th annual conference on Genetic and evolutionary computation, 2013This work presents an implementation of the asynchronous island model suitable for multi-objective evolutionary optimization on heterogeneous and large-scale computing platforms. The migration of individuals is regulated by the crowding comparison operator applied to the originating population during selection and to the receiving population augmented ...
Marcus Märtens, Dario Izzo
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Bug localization in software using NSGA-II
2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2018Finding bugs in a software is a cumbersome and tedious task. When a new bug is reported, the developers find it challenging to replicate the unexpected behavior of the software, in order to fix the original fault. In this paper, an automated model is presented to find and sort the classes present in the source code according to their proneness of ...
Ruchika Malhotra +3 more
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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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Materials and Emerging Technologies for Sustainability
This study presents a comparative analysis of construction site layout planning using the NSGA-II and hybrid NSGA-II-MOPSO multi-objective optimization algorithms. The objective is to simultaneously minimize material transportation distance and maximize on-site safety by optimizing the spatial arrangement of auxiliary facilities and tower cranes ...
Xuan Thanh Nguyen +4 more
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This study presents a comparative analysis of construction site layout planning using the NSGA-II and hybrid NSGA-II-MOPSO multi-objective optimization algorithms. The objective is to simultaneously minimize material transportation distance and maximize on-site safety by optimizing the spatial arrangement of auxiliary facilities and tower cranes ...
Xuan Thanh Nguyen +4 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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A comprehensive survey on NSGA-II for multi-objective optimization and applications
Artificial Intelligence Review, 2023Haiping Ma +4 more
semanticscholar +1 more source