Evolution strategies for evolving artificial neural networks in an arcade game - Tse, Guan Tan; Teo, Jason; Anthony, Patricia (2010)
The aim of this paper is to use a simple but powerful evolutionary algorithm called Evolution Strategies (ES) to evolve the connection weights and biases of feed-forward artificial neural networks (ANN) and to examine its learning ability through computational experiments in a non-deterministic and dynamic environment, which is the well-known arcade game called Ms. Pac-man.The resulting algorithm is referred to as an Evolution Strategies Neural Network or ESNet.This study is an attempt to create an autonomous intelligent controller to play the game.The comparison of ESNet with two random systems, Random Direction (RandDir) and Random Neural Network (RandNet) yields promising results.