Machine Learning Algorithms for Improving Black Box Optimization Solvers
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
arXiv:2609.01493v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-w...
arXiv:2609.22329v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation...
The paper introduces DRL-DCO, a Deep Reinforcement Learning‑governed dynamic orchestration of Differential Evolution and CMA‑ES for high‑dimensional structural model updating. A DDPG actor‑critic agent uses a progression‑aware state and diversity‑informed reward to continuously reallocate resources between exploration and exploitation, while controlling population size, elite preservation, and restarts. The trained actor can operate in a supervision‑free inference mode, enabling fast deployment with maintained performance, and demonstrates superior convergence accuracy and robustness on benchmark optimization and structural health monitoring problems.
arXiv:2602.00532v2 Announce Type: replace-cross Abstract: Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often pro...
arXiv:2609. 22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples.