Hypernetwork-Based Multi-Objective Optimization with NSGA-II

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{he:2024:GECCOcomp,

• author = "Jie He and Yaohong Zhang and Ji Wang and Xiaoqing Li and Weidong Bao",
• title = "{Hypernetwork-Based} {Multi-Objective} Optimization with {NSGA-II}",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Aneta Neumann and Elizabeth Wanner",
• pages = "431--434",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, multi-objective optimization, NSGA-II, crowing-distance assignment: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654189",
• size = "4 pages",
• abstract = "Multi-objective optimization algorithms have a wide range of applications in various fields. NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a well-known multi-objective optimization algorithm. However, when solving high-dimensional problems using NSGA-II, the algorithm complexity increases linearly with the problem dimension. To address this issue, this paper proposes the HNSGA-II (Hypernetwork Non-dominated Sorting Genetic Algorithm II) algorithm based on hypernetwork modeling. The HNSGA-II algorithm introduces a binary tree data structure and uses hypernetworks to model candidate solutions. A new crowding calculation is designed to improve the algorithm's convergence. Horizontal and vertical comparative experiments were conducted on standard test sets ZDT and BT using the newly proposed algorithm, NSGA-II, MOEAD, and other algorithms. The results demonstrate that the HNSGA-II algorithm achieves a maximum improvement of about 10\% in time efficiency when solving high-dimensional problems and exhibits better algorithmic convergence than the original NSGA-II algorithm.",
• notes = "GECCO-2024 GA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jie He Yaohong Zhang Ji Wang Xiaoqing Li Weidong Bao

Citations