[HTML][HTML] Recent advances in selection hyper-heuristics
Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …
for computational search problems. This is in contrast to many approaches, which represent …
A comprehensive survey on optimizing deep learning models by metaheuristics
Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn
higher levels of feature hierarchy established by lower level features by transforming the raw …
higher levels of feature hierarchy established by lower level features by transforming the raw …
A reinforcement learning-variable neighborhood search method for the capacitated vehicle routing problem
Finding the best sequence of local search operators that yields the optimal performance of
Variable Neighborhood Search (VNS) is an important open research question in the field of …
Variable Neighborhood Search (VNS) is an important open research question in the field of …
Lights and shadows in evolutionary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges
Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …
Learning models for several purposes: from the discovery of network topologies and …
Deep learning algorithms for machinery health prognostics using time-series data: A review
Background An intelligent predictive health management paradigm for industrial machinery
is inevitable in Industry 4.0. The machinery health failure/degradation data acquired as time …
is inevitable in Industry 4.0. The machinery health failure/degradation data acquired as time …
Hyper-heuristic local search for combinatorial optimisation problems
Local search algorithms have been successfully used for many combinatorial optimisation
problems. The choice of the most suitable local search algorithm is, however, a challenging …
problems. The choice of the most suitable local search algorithm is, however, a challenging …
[HTML][HTML] Evolutionary algorithm-based iterated local search hyper-heuristic for combinatorial optimization problems
SA Adubi, OO Oladipupo, OO Olugbara - Algorithms, 2022 - mdpi.com
Hyper-heuristics are widely used for solving numerous complex computational search
problems because of their intrinsic capability to generalize across problem domains. The fair …
problems because of their intrinsic capability to generalize across problem domains. The fair …
Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search
Methaheuristics (MHs) are techniques widely used for solving complex optimization
problems. In recent years, the interest in combining MH and machine learning (ML) has …
problems. In recent years, the interest in combining MH and machine learning (ML) has …
Hyper heuristic evolutionary approach for constructing decision tree classifiers
Decision tree models have earned a special status in predictive modeling since these are
considered comprehensible for human analysis and insight. Classification and Regression …
considered comprehensible for human analysis and insight. Classification and Regression …
Optimising deep learning by hyper-heuristic approach for classifying good quality images
Abstract Deep Convolutional Neural Network (CNN), which is one of the prominent deep
learning methods, has shown a remarkable success in a variety of computer vision tasks …
learning methods, has shown a remarkable success in a variety of computer vision tasks …