Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

The deep learning revolution has greatly been accelerated by the'hardware lottery': Recent
advances in modern hardware accelerators, compilers and the availability of open-source …

Genetic algorithms constitute a family of black-box optimization algorithms, which take
inspiration from the principles of biological evolution. While they provide a general-purpose …

Large Transformer models are capable of implementing a plethora of so-called in-context
learning algorithms. These include gradient descent, classification, sequence completion …

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 …

Abstract Recently, the Deep Learning community has become interested in evolutionary
optimization (EO) as a means to address hard optimization problems, eg meta-learning …

Abstract Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-
RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine …

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …

It is often very tedious for human experts to design efficient algorithms. Recently, we have
proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for …

Optimization can be found in many real-life applications. Designing an effective algorithm for
a specific optimization problem typically requires a tedious amount of effort from human …