A Novel Memetic Feature Selection Algorithm [PDF]
, 2013 Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity.
Faraahi, Ahmad +3 more
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A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security
IEEE Access, 2018 Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems.
Nasser R. Sabar, Xun Yi, Andy Song
doaj +1 more source
Non-linear great deluge with learning mechanism for solving the course timetabling problem [PDF]
, 2009 International ...
Landa-Silva, D. +3 more
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A learning automata based multiobjective hyper-heuristic [PDF]
, 2017 Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation problems. However, once implemented, their application to a new problem domain or a slight change in the problem
John, Robert, Li, Wenwen, Özcan, Ender
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Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation [PDF]
, 2002 The term hyperheuristic was introduced by the authors as a high-level heuristic that adaptively controls several low-level knowledgepoor heuristics so that while using only cheap, easy-to-implement low-level heuristics, we may achieve solution quality approaching that of an expensive knowledge-rich approach. For certain classes of problems, this allows
Peter I. Cowling +2 more
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EEG Motor Imagery Classification by Feature Extracted Deep 1D-CNN and Semi-Deep Fine-Tuning
IEEE AccessThe main goal of this paper is to introduce a Motor Imagery (MI) classification system for electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose using a feature-extracted deep one-dimension (1D) convolutional neural ...
Mohamad Taghizadeh +2 more
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Application of population evolvability in a hyper-heuristic for dynamic multi-objective optimization
Technological and Economic Development of Economy, 2019 It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current ...
Teodoro Macias-Escobar +5 more
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An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex [PDF]
, 2014 Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for ...
Asta, Shahriar, Özcan, Ender
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A study of jamming resource allocation based on a hyperheuristic framework
AIP AdvancesThere are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation.
Zelong Hao, Xing Wang, You Chen
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Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling [PDF]
, 2007 In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task.
Remde, Stephen M. +3 more
openaire +2 more sources