Results 51 to 60 of about 1,226,644 (182)

A graph-based hyper heuristic for timetabling problems [PDF]

, 2006
This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling.
Amnon Meisels, Burke, Edmund, MacCloumn, Barry, Rong Qu, Qu, R., Sanja Petrovic, Petrovic, Sanja, Meisels, A., Burke, E.K., Barry McCollum, Edmund K Burke, Petrovic, S., Meisels, Amnon, Qu, Rong, McCollum, Barry   +14 more
core   +1 more source

A Multi‐Sequence Adversarial Fusion U‐Net for Brain Tumor Image Segmentation

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
In the field of brain tumor image segmentation, in order to avoid the impact of insufficient number of training samples, the method of fusing multi‐modal MRI information before segmentation is widely used. However, when fusing different modal features, existing methods only add fixed weights to the features of each modality, resulting in insufficient ...
Jie Wang, Jinglu Hu
wiley   +1 more source

Application of Heuristic Combinations within a Hyper-Heuristic Framework for Exam Timetabling

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2020
Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem.
Gabriella Icasia, Raras Tyasnurita, Etria Sepwardhani Purba   +2 more
doaj   +1 more source

Heuristics-based Design Process [PDF]

, 2014
The authors would like to thank the Research Department of Universidad EAFIT, and in particular the Research Group in Design Engineering for their participation in this research.
CALLE-ESCOBAR, Manuela, CALLE-ESCOBAR, , Manuela, PAILHES, Jerome, Calle-Escobar,, Manuela, MEJÍA-GUTIÉRREZ, Ricardo, NADEAU, Jean-Pierre   +5 more
core  

Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph contrastive learning (GCL) relies on acquiring high‐quality positive and negative samples to learn the structural semantics of the input graph. Previous approaches typically sampled negative samples from the same training batch or an irrelevant external graph.
Haoran Yang, Hongxu Chen, Xiangyu Zhao, Sixiao Zhang, Xiangguo Sun, Qian Li, Hongzhi Yin, Guandong Xu   +7 more
wiley   +1 more source

Robust Multi‐Source Batch Normalisation for Test‐Time Batch Adaptation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Test‐Time Batch Adaptation (TTBA) aims to adapt a pre‐trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre‐trained model or the target data in a one‐to‐one alignment paradigm. However, these one‐to‐one alignment paradigms assume that the source domain may share the same knowledge ...
Xinlin Xiao, Xiangtao Zheng, Jianhua Tao, Xiaoqiang Lu   +3 more
wiley   +1 more source

Robotic Mobile Fulfillment Systems: Are Hyper-Heuristics a Feasible Approach When Dealing With the Pod Allocation and Item Storage Problems?

IEEE Access
The rise of online shopping has forced warehouses to improve their operations to keep up with demand. In this work, we focus on a warehouse type known as the Robotic Mobile Fulfillment System (RMFS).
Toby Yung, Maria Torcoroma Benavides-Robles, Jose Carlos Ortiz-Bayliss, Ivan Amaya   +3 more
doaj   +1 more source

Novel Hyper-heuristics Applied to the Domain of Bin Packing [PDF]

Principal to the ideology behind hyper-heuristic research is the desire to increase the level of generality of heuristic procedures so that they can be easily applied to a wide variety of problems to produce solutions of adequate quality within practical
Sim, Kevin
core  

Sub‐optimal Internet of Thing devices deployment using branch and bound method

IET Networks, EarlyView.
The main contributions of this paper are (1) IoT network deployment problem formation as MILP problem to optimise the transmission among network nodes, and (2) New BB method with a machine learning function to reduce the computational complexity. Abstract The Internet of Thing (IoT) network deployments are widely investigated in 4G and 5G systems and ...
Haesik Kim
wiley   +1 more source

NeuHH: A Neuromorphic-Inspired Hyper-Heuristic Framework for Solving the Capacitated Single-Allocation p-Hub Location Routing Problem

Vehicles
This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub ...
Kassem Danach, Hassan Harb, Semaan Amine, Mariem Belhor   +3 more
doaj   +1 more source