Zobrazeno 1 - 10
of 1 249
pro vyhledávání: '"Munetomo A"'
This paper introduces a novel competitive mechanism into differential evolution (DE), presenting an effective DE variant named competitive DE (CDE). CDE features a simple yet efficient mutation strategy: DE/winner-to-best/1. Essentially, the proposed
Externí odkaz:
http://arxiv.org/abs/2406.05436
Motivated by the potential of large language models (LLMs) as optimizers for solving combinatorial optimization problems, this paper proposes a novel LLM-assisted optimizer (LLMO) to address adversarial robustness neural architecture search (ARNAS),
Externí odkaz:
http://arxiv.org/abs/2406.05433
Autor:
Zhang, Enzhi, Lyngaas, Isaac, Chen, Peng, Wang, Xiao, Igarashi, Jun, Huo, Yuankai, Wahib, Mohamed, Munetomo, Masaharu
Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear se
Externí odkaz:
http://arxiv.org/abs/2404.09707
Autor:
Munetomo, Sosuke1 (AUTHOR), Uchiyama, Jumpei2 (AUTHOR) uchiyama@okayama-u.ac.jp, Takemura-Uchiyama, Iyo2 (AUTHOR), Wanganuttara, Thamonwan2 (AUTHOR), Yamamoto, Yumiko2 (AUTHOR), Tsukui, Toshihiro3 (AUTHOR), Hagiya, Hideharu4 (AUTHOR), Kanamaru, Shuji5 (AUTHOR), Kanda, Hideyuki1 (AUTHOR), Matsushita, Osamu2 (AUTHOR)
Publikováno v:
PLoS ONE. 10/23/2024, Vol. 19 Issue 10, p1-16. 16p.
In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitnes
Externí odkaz:
http://arxiv.org/abs/2210.06814
In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the participation of a well-performed individual as an elite can accelerate the c
Externí odkaz:
http://arxiv.org/abs/2209.13077
Autor:
Zhong, Rui, Munetomo, Masaharu
Many optimization problems suffer from noise, and nonlinearity check-based decomposition methods (e.g. Differential Grouping) will completely fail to detect the interactions between variables in multiplicative noisy environments, thus, it is difficul
Externí odkaz:
http://arxiv.org/abs/2209.00777
Autor:
Zhong, Rui, Munetomo, Masaharu
In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, which are ordinary averaging strategy, and weighted averaging strategy. W
Externí odkaz:
http://arxiv.org/abs/2208.14619
Autor:
Zhong, Rui, Munetomo, Masaharu
In this paper, we propose a variable grouping method based on cooperative coevolution for large-scale multi-objective problems (LSMOPs), named Linkage Measurement Minimization (LMM). And for the sub-problem optimization stage, a hybrid NSGA-II with a
Externí odkaz:
http://arxiv.org/abs/2208.13415
Publikováno v:
Alexandria Engineering Journal, Vol 87, Iss , Pp 148-163 (2024)
Vegetation evolution (VEGE) is a newly proposed meta-heuristic algorithm (MA) with excellent exploitation but relatively weak exploration capacity. We thus focus on further balancing the exploitation and the exploration of VEGE well to improve the ov
Externí odkaz:
https://doaj.org/article/0d6a8f8081a0443598d060b8dfbb0f73