Solving dynamic optimization problems (DOPs) induced by time-varying optimization
objective functions and constraints is challenging. Quantum computation has received …

In practice, stock market behavior is difficult to predict accurately because of its high
volatility. To improve market forecasts, a method inspired by Elman neural network and …

The selection mechanism of genetic algorithms can play a key role in leading the
optimization process towards suitable solutions of a given problem, as their application can …

Quantum computers can be a revolutionary tool to implement inference engines for fuzzy
rule-based systems. In fact, the use of quantum mechanical principles can enable parallel …

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult
task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic …

Quantum computing has entered its mature life thanks to the availability of cloud-based
Noisy Intermediate-Scale Quantum (NISQ) technologies. These devices allow quantum …

Quantum-inspired particle swarm optimization (QPSO) is a powerful computation technique
that introduces quantum mechanics theory into particle swarm optimization (PSO), which …

Quantum neural networks have made progress in classification tasks. However, they face
challenges when applied to multi-class image classification tasks. In this paper, we propose …

Feature selection is critical in machine learning to reduce dimensionality and improve model
accuracy and efficiency. The exponential growth in feature space dimensionality for modern …

A comparative study is presented regarding the evolutionary design of quantum operators in
the form of unitary matrices. A comparative study is presented regarding the evolutionary …