Reevo: Large language models as hyper-heuristics with reflective evolution
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …
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 …
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 …
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 …
Toward green and human-like artificial intelligence: A complete survey on contemporary few-shot learning approaches
Despite deep learning's widespread success, its data-hungry and computationally
expensive nature makes it impractical for many data-constrained real-world applications …
expensive nature makes it impractical for many data-constrained real-world applications …
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 …
Parco: Learning parallel autoregressive policies for efficient multi-agent combinatorial optimization
Multi-agent combinatorial optimization problems such as routing and scheduling have great
practical relevance but present challenges due to their NP-hard combinatorial nature, hard …
practical relevance but present challenges due to their NP-hard combinatorial nature, hard …
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) …