Towards certifiable ai in aviation: landscape, challenges, and opportunities
Artificial Intelligence (AI) methods are powerful tools for various domains, including critical
fields such as avionics, where certification is required to achieve and maintain an …
fields such as avionics, where certification is required to achieve and maintain an …
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 …
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) …
Meta-Black-Box-Optimization through Offline Q-function Learning with Mamba Architecture
Recent progress in Meta-Black-Box-Optimization (MetaBBO) has demonstrated that meta-
training a neural network based meta-level control policy over an optimization task …
training a neural network based meta-level control policy over an optimization task …