Reinforcement-Learning-Based Intrusion Detection in Communication Networks: A Review
Modern communication networks have to meet the performance requirements of
contemporary industrial control systems (ICSs), which are increasingly being connected to …
contemporary industrial control systems (ICSs), which are increasingly being connected to …
Meta-Black-Box optimization for evolutionary algorithms: Review and perspective
Abstract Black-Box Optimization (BBO) is increasingly vital for addressing complex real-
world optimization challenges, where traditional methods fall short due to their reliance on …
world optimization challenges, where traditional methods fall short due to their reliance on …
Auto-configuring exploration-exploitation tradeoff in evolutionary computation via deep reinforcement learning
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers,
leverage a group of individuals to cooperatively search for the optimum. The exploration …
leverage a group of individuals to cooperatively search for the optimum. The exploration …
Rlemmo: Evolutionary multimodal optimization assisted by deep reinforcement learning
Solving multimodal optimization problems (MMOP) requires finding all optimal solutions,
which is challenging in limited function evaluations. Although existing works strike the …
which is challenging in limited function evaluations. Although existing works strike the …
Neural exploratory landscape analysis
Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained
neural networks can effectively guide the design of black-box optimizers, significantly …
neural networks can effectively guide the design of black-box optimizers, significantly …
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging
avenue within the Evolutionary Computation (EC) community, which incorporates Meta …
avenue within the Evolutionary Computation (EC) community, which incorporates Meta …
Advancing photovoltaic system design: An enhanced social learning swarm optimizer with guaranteed stability
L Deng, S Liu - Computers in Industry, 2025 - Elsevier
Parameter estimation of photovoltaic (PV) models, mathematically, is a typical complicated
nonlinear multimodal optimization problem with box constraints. Although various …
nonlinear multimodal optimization problem with box constraints. Although various …
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Zero-shot optimization involves optimizing a target task that was not seen during training,
aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It …
aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It …
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the
potential of using neural networks to dynamically configure evolutionary algorithms (EAs) …
potential of using neural networks to dynamically configure evolutionary algorithms (EAs) …
Competition-Based Stochastic Learning Particle Swarm Optimization Algorithm for Large-Scale Optimization Problem
With the development of information technology, large-scale optimization problem is the
core issue in various applications. Due to the high dimension characteristics, large-scale …
core issue in various applications. Due to the high dimension characteristics, large-scale …