Hot off the Press: Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Many Objectives
Author:
Affiliation:
1. Harbin Institute of Technology, Shenzhen, China
2. Institut Polytechnique de Paris, Paris, France
Funder
Agence Nationale de la Recherche
Guangdong Provincial Department of Science and Technology
Science, Technology and Innovation Commission of Shenzhen Municipality
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3638530.3664061
Reference26 articles.
1. SMS-EMOA: Multiobjective selection based on dominated hypervolume
2. Chao Bian and Chao Qian. 2022. Better running time of the non-dominated sorting genetic algorithm II (NSGA-II) by using stochastic tournament selection. In Parallel Problem Solving From Nature, PPSN 2022. Springer, 428--441.
3. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
4. Kalyanmoy Deb, Manikanth Mohan, and Sikhar Mishra. 2003. A fast multi-objective evolutionary algorithm for finding well-spread Pareto-optimal solutions. KanGAL report 2003002 (2003).
5. A fast and elitist multiobjective genetic algorithm: NSGA-II
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