Results 81 to 90 of about 7,687 (217)
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 +4 more
core +3 more sources
AlgorithmsThis 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 +4 more
doaj +1 more source
Automated construction of evolutionary algorithm operators for the bi-objective water distribution network design problem using a genetic programming based hyper-heuristic approach [PDF]
, 2015 The water distribution network (WDN) design problem is primarily concerned with finding the optimal pipe sizes that provide the best service for minimal cost; a problem of continuing importance both in the UK and internationally.
Keedwell, Edward +3 more
core +1 more source
Evolution of group-theoretic cryptology attacks using hyper-heuristics
Journal of Mathematical Cryptology, 2021 In previous work, we developed a single evolutionary algorithm (EA) to solve random instances of the Anshel–Anshel–Goldfeld (AAG) key exchange protocol over polycyclic groups. The EA consisted of six simple heuristics which manipulated strings.
Craven Matthew J., Woodward John R.
doaj +1 more source
A Universal Meta‐Heuristic Framework for Influence Maximisation in Hypergraphs
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Influence maximisation (IM) aims to select a small number of nodes that are able to maximise their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in simple networks, higher‐order interactions between entities in various real‐world systems are usually not taken into ...
Ming Xie +5 more
wiley +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 +7 more
wiley +1 more source
Multi-objective optimisation with a sequence-based selection hyper-heuristic [PDF]
, 2016 Hyper-heuristics have been used widely to solve optimisation problems, often single-objective and discrete in nature. Herein, we extend a recently-proposed selection hyper-heuristic to the multiobjective domain and with it optimise continuous problems ...
Keedwell, EK, Walker, DJ
core +1 more source
Reinforcement learning based local search for grouping problems: A case study on graph coloring [PDF]
, 2016 Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally ...
Duval, Béatrice +2 more
core +5 more sources
IEEE AccessThe urgent demand to reduce carbon emissions due to global warming has driven innovative approaches in cloud computing. This paper introduces the Hyper-Heuristic for Cloud Scheduling Problems (HHCSP), a hyper-heuristic designed to optimize tasks in cloud
Vinicius Renan De Carvalho +1 more
doaj +1 more source
A cooperative hyper-heuristic search framework [PDF]
Journal of Heuristics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ouelhadj, Djamila, Petrovic, S.
openaire +3 more sources