Algorithm parameters, in particular hyperparameters of machine learning algorithms, can
substantially impact their performance. To support users in determining well-performing …

For any machine learning model, finding the optimal hyperparameter setting has a direct
and significant impact on the model's performance. In this paper, we discuss different types …

In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much
popularity in the evolutionary computation community for solving optimization problems …

Profiled side-channel analysis (SCA) leverages leakage from cryptographic
implementations to extract the secret key. When combined with advanced methods in neural …

Differential evolution (DE) and Estimation of distribution algorithm (EDA) exhibit
complementary superiority in solving complex continuous optimization and engineering …

The vision behind this paper looks ahead to evolutionary robot systems where morphologies
and controllers are evolved together and 'newborn'robots undergo a learning process to …

This work presents the starfish optimization algorithm (SFOA), a novel bio-inspired
metaheuristic for solving optimization problems, which simulates behaviors of starfish …

This paper studies the allocation of shared resources between ultra-reliable low-latency
communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and …

Centralized traffic signal control has long been a challenging, high‐dimensional
optimization problem. This study establishes a simulation‐based optimization framework …

Green bonds are powerful tools for fighting against climate change and typically exhibit
more volatility than conventional bonds do. However, the volatility forecasting of green bond …