Evolution and self-teaching in neural networks
Nam Do-Hoang Le · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
Previous work presented a technique called evolving self-taught neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward [3]. In an autonomous multi-agent setting in which the agent is primitively set to know little or nothing about its environment, self-teaching was shown to give rise to intelligence, whereas an evolutionary algorithm alone fails since it has no way to search without gradient information. In this paper, we conduct another comparative experiment in which the foraging agent is built more conscious of its environment beforehand. Experimental results show that the more conscious primitive design can let evolution alone be able to search. Yet the combination of evolution and self-teaching still outperforms the alternative. Indications for future work on evolving intelligence are also presented.