Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Deep Learning has enabled remarkable progress over the last years on a variety of tasks,
such as image recognition, speech recognition, and machine translation. One crucial aspect …

A grand challenge in reinforcement learning is intelligent exploration, especially when
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …

Evolution provides a creative fount of complex and subtle adaptations that often surprise the
scientists who discover them. However, the creativity of evolution is not limited to the natural …

Deep reinforcement learning (deep RL) combines deep learning and reinforcement
learning, in which artificial agents learn to solve sequential decision-making problems. In the …

The job shop scheduling problem (JSSP) with dynamic events and uncertainty is a strongly
NP-hard combinatorial optimization problem (COP) with extensive applications in the …

We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD)
framework for optimizing deep neural networks. ESGD combines SGD and gradient-free …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

Evolutionary computation has largely exhibited its potential to complement conventional
learning algorithms in a variety of machine learning tasks, especially those related to …