Results 91 to 100 of about 3,855 (254)

A software interface for supporting the application of data science to optimisation [PDF]

, 2015
Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value ...
EK Burke, G Ochoa, G Ochoa, M Maashi, S Asta   +4 more
core   +3 more sources

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

Hybridizations within a graph based hyper-heuristic framework for university timetabling problems [PDF]

, 2008
A significant body of recent literature has explored various research directions in hyper-heuristics (which can be thought as heuristics to choose heuristics). In this paper, we extend our previous work to construct a unified graph-based hyper-heuristic (
Burke, Edmund, Qu, Rong
core   +2 more sources

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

Neural Network Repair With Shapley‐Guided Search

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT The deployment of deep neural networks (DNNs) in safety‐critical domains is critically hampered by their vulnerability to defects, which can arise from malicious attacks or low‐quality data. Therefore, precisely locating the network components responsible for these defects, and subsequently repairing them without compromising overall model ...
Xiaofu Du, Zixiong Zhang, Xuesong Wang, Linqiang Liu, Junyan Qian   +4 more
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

Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool [PDF]

, 2012
In this paper we present a new version of a GP financial forecasting tool, called EDDIE 8. The novelty of this version is that it allows the GP to search in the space of indicators, instead of using pre-specified ones.
Austin M., Binner J., Bouchachia A., Burke E., Cao L., Dempsey I., Edwards R., Hart E., Kampouridis M., Kim K., Koza J., Lam M., Marcek D., O'Neill M., Roh T., Sapankevych N., Tsang E., Tsang E., Özcan E.   +18 more
core   +3 more sources

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

‘But I can't preregister my research’: Improving the reproducibility and transparency of ecology and conservation with adaptive preregistration for model‐based research

Methods in Ecology and Evolution, EarlyView.
Abstract Preregistration is an open science practice which aims to improve research transparency and mitigate questionable research practices, like cherry‐picking results. It helps protect against cognitive biases, like hindsight bias, that can influence how study outcomes are interpreted.
Elliot Gould, Christopher S. Jones, Jian D. L. Yen, Hannah S. Fraser, Henry F. Wootton, Megan K. Good, David H. Duncan, Cindy E. Hauser, Bonnie C. Wintle, Libby Rumpff   +9 more
wiley   +1 more source

Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics

IEEE Access
Scheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed ...
Alonso Vela, Gerardo Humberto Valencia-Rivera, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Ivan Amaya   +4 more
doaj   +1 more source