A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics
The application of artificial intelligence in everyday life is becoming all-pervasive and
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …
Estimation of distribution algorithms in machine learning: a survey
P Larrañaga, C Bielza - IEEE Transactions on Evolutionary …, 2023 - ieeexplore.ieee.org
The automatic induction of machine learning models capable of addressing supervised
learning, feature selection, clustering and reinforcement learning problems requires …
learning, feature selection, clustering and reinforcement learning problems requires …
[HTML][HTML] Deep learning in business analytics and operations research: Models, applications and managerial implications
Business analytics refers to methods and practices that create value through data for
individuals, firms, and organizations. This field is currently experiencing a radical shift due to …
individuals, firms, and organizations. This field is currently experiencing a radical shift due to …
Learning a latent search space for routing problems using variational autoencoders
Methods for automatically learning to solve routing problems are rapidly improving in
performance. While most of these methods excel at generating solutions quickly, they are …
performance. While most of these methods excel at generating solutions quickly, they are …
A hybrid modelling method for time series forecasting based on a linear regression model and deep learning
W Xu, H Peng, X Zeng, F Zhou, X Tian, X Peng - Applied Intelligence, 2019 - Springer
Time series forecasting has important theoretical significance and engineering application
value. A number of studies have shown that hybrid modelling is very successful in various …
value. A number of studies have shown that hybrid modelling is very successful in various …
[HTML][HTML] A novel deep belief network architecture with interval type-2 fuzzy set based uncertain parameters towards enhanced learning
This paper proposes a novel Deep Belief Network (DBN) architecture, the 'Interval Type-2
Fuzzy DBN (IT2FDBN)', which models the weights and biases with IT2 FSs. Thus, it …
Fuzzy DBN (IT2FDBN)', which models the weights and biases with IT2 FSs. Thus, it …
Learning to Prune Instances of k-median and Related Problems
In a large number of industrial applications, combinatorial optimization problems are
repeatedly solved with datasets from similar distribution. In recent years, machine learning …
repeatedly solved with datasets from similar distribution. In recent years, machine learning …
Learning fine-grained search space pruning and heuristics for combinatorial optimization
Combinatorial optimization problems arise naturally in a wide range of applications from
diverse domains. Many of these problems are NP-hard and designing efficient heuristics for …
diverse domains. Many of these problems are NP-hard and designing efficient heuristics for …
Restricted Boltzmann machine-driven interactive estimation of distribution algorithm for personalized search
Effective and efficient personalized search is one of the most pursued objectives in the era of
big data. The challenge of this problem lies in its complex quantifying evaluations and …
big data. The challenge of this problem lies in its complex quantifying evaluations and …
Interval type-2 fuzzy sets for enhanced learning in deep belief networks
Restricted Boltzmann Machine (RBM) is a generative, stochastic neural network with two
separate layers of hidden and visible units. Training data samples in RBM are usually …
separate layers of hidden and visible units. Training data samples in RBM are usually …