Results 81 to 90 of about 1,200,755 (251)

Multi‐Agent Reinforcement Learning for Joint Police Patrol and Dispatch

Naval Research Logistics (NRL), EarlyView.
ABSTRACT Police patrol units need to split their time between performing preventive patrol and being dispatched to serve emergency incidents. In the existing literature, patrol and dispatch decisions are often studied separately. We consider joint optimization of these two decisions to improve police operations efficiency and reduce response time to ...
Matthew Repasky, He Wang, Yao Xie
wiley   +1 more source

Hyper-heuristic online learning for self-assembling swarm robots

, 2018
© Springer International Publishing AG, part of Springer Nature 2018. A robot swarm is a solution for difficult and large scale tasks. However, controlling and coordinating a swarm of robots is challenging, because of the complexity and uncertainty of ...
S Yu (7291433), A Aleti (13535641), Jan Carlo Barca (13060149), A Song (9570050)   +3 more
core   +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

Hyper Heuristic Memetic-Algorithm Based Optimization of Solar Photovoltaic Systems [PDF]

E3S Web of Conferences
For identifying maximum power tracking by using a solar PV system, a modified solar panel is designed with the support of reflector. Based on diameter and size, reflector is selected.
Deshmukh Rajesh Keshavrao, Shrivastav Mohit   +1 more
doaj   +1 more source

Interplanetary frontiers: terraforming from an invasion science perspective

Oikos, EarlyView.
The pursuit of a multi‐planetary existence represents one of humanity's greatest frontiers. If applied justly, it offers an opportunity to extend its civilization's lifespan amid escalating sustainability crises on Earth. One approach increasingly gaining traction is terraforming, a hitherto theoretical ecological and evolutionary experiment revolving ...
Teun Everts, Ronaldo Sousa, Phillip J. Haubrock   +2 more
wiley   +1 more source

A Lifelong Learning Hyper-heuristic Method for Bin Packing

, 2015
We describe a novel Hyper-heuristic system which continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; representative problems and ...
Sim, Kevin, Hart, Emma, Paechter, Ben
core   +1 more source

Student Surpasses the Teacher: Apprenticeship Learning for Quadratic Unconstrained Binary Optimisation

Algorithms
This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem.
Jack Cakebread, Warren G. Jackson, Daniel Karapetyan, Andrew J. Parkes, Ender Özcan   +4 more
doaj   +1 more source

Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling [PDF]

, 2002
This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyper-heuristics has recently been employed to refer to 'heuristics that choose heuristics' rather than heuristics that ...
R. Qu, MacCarthy, Bart L., Burke, Edmund, Qu, Rong, B. L. Maccarthy, E. K. Burke, Petrovic, Sanja, S. Petrovic   +7 more
core  

A simulated annealing hyper-heuristic methodology for flexible decision support

, 2011
Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate.
Kendall, Graham, Edmund K. Burke, Barry McCollum, Burke, Edmund, Graham Kendall, Bai, Ruibin, Blazewicz, Jacek, McCollum, Barry, Ruibin Bai, Jacek Blazewicz   +9 more
core   +1 more source

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