Cultivating Diversity: A Comparison of Diversity Objectives in Neuroevolution

Abstract

Inspired by biological evolution's ability to produce the complexity that is human brains, neuroevolution utilizes evolutionary algorithms for optimizing the hyperparameters and structure of neural networks. However, evolutionary algorithms fail to produce the same type of diversity as biological evolution can with the abundant range of adaptable and complex organisms in nature. Encouraging diversity in neuroevolution has seen increased interest in recent years with methods such as Novelty Search and Quality-Diversity optimization. Another promising, but less explored approach, is to explicitly encourage diversity with an additional diversity objective. There is, however, a lack of knowledge regarding the relationship between the type of diversity encouraging objective and the characteristics of the targeted problem. For instance, should a diversity of brain structures, behaviors, or neural firing patterns be encouraged when optimizing a walking robot? Discussion6.1 Differences between objectives of the same type of diversity . 6.1.1 Ad hoc behavioral diversity outperforms generic behavioral diversity .