Evolutionary algorithms (EA), a class of stochastic search methods based on the principles
of natural evolution, have received widespread acclaim for their exceptional performance in …

Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to
find a set of high-performing, yet diverse solutions. They have found many successful …

Combining Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for policy
search has been proven to improve RL performance. However, previous works largely …

Training large language models to acquire specific skills remains a challenging endeavor.
Conventional training approaches often struggle with data distribution imbalances and …

A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for
adapting to unexpected situations. Over the past decade, advancements in deep …

Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with
the aim of generating a set of high-quality and diverse solutions. To achieve such a …

Both Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) have demonstrated
powerful capabilities in policy search with different principles. A promising direction is to …

Single-objective optimization algorithms search for the single highest-quality solution with
respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance …