Neuroevolution, ie evolution of artificial neural networks, has recently emerged as a
powerful technique for solving challenging reinforcement learning problems. Compared to …

Neuroevolution is a machine learning technique that applies evolutionary algorithms to
construct artificial neural networks, taking inspiration from the evolution of biological nervous …

Neuroevolution is currently the strongest method on the pole-balancing benchmark
reinforcement learning tasks. Although earlier studies suggested that there was an …

Increase the performance of various neural network architectures using NEAT, HyperNEAT,
ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution Key FeaturesImplement …

Artificial neural networks can potentially control autonomous robots, vehicles, factories, or
game players more robustly than traditional approaches. Neuroevolution, ie the artificial …

Evolution of neural networks, or neuroevolution, bas been successful on many low-level
control problems such as pole balancing, vehicle control, and collision warning. However …

An important question in neuroevolution is how to gain an advantage from evolving neural
network topologies along with weights. We present a method, NeuroEvolution of …

In addition to their undisputed success in solving classical optimization problems,
neuroevolutionary and population-based algorithms have become an alternative to standard …

The idea of using evolutionary computation to train artificial neural networks, or
neuroevolution (NE), for reinforcement learning (RL) tasks has now been around for over 20 …

NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial
Neural Networks. NeuroEvolution is thought to possess many benefits over traditional …