Evolving Spiking Neural Networks of artificial creatures using Genetic Algorithm
Proceedings of the International Joint Conference on Neural Networks
This paper presents a Genetic Algorithm (GA) based evolution framework in which Spiking Neural Network (SNN) of single or a colony of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed of randomly connected Izhikevich spiking reservoir neural networks. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulation results prove that the evolutionary algorithm has the capability to find or synthesis artificial creatures which can survive in the environment successfully and also simulations verify that colony approach has a better performance in comparison with a single complex creature.
Eskandari, Elahe; Ahmadi, Arash; Gomar, Shaghayegh; Ahmadi, Majid; and Saif, Mehrdad. (2016). Evolving Spiking Neural Networks of artificial creatures using Genetic Algorithm. Proceedings of the International Joint Conference on Neural Networks, 2016-October, 411-418.