Cultivating Diversity: A Comparison of Diversity Objectives in Neuroevolution

Inspired by biological evolution’s ability to produce complex and intelligent beings, neuroevolution utilizes evolutionary algorithms for optimizing the connection weights and structure of artificial neural networks. With evolutionary algorithms often failing to produce the same level of diversity as biological evolution, explicitly encouraging diversity with additional optimization objectives has emerged as a successful approach. However, there is a lack of knowledge regarding the performance of different types of diversity objectives on problems with different characteristics. In this paper, we perform a systematic comparison between objectives related to structural diversity, behavioral diversity, and our newly proposed representational diversity. We explore these objectives’ effects on problems with different levels of modularity, regularity, deceptiveness and discreteness and find clear relationships between problem characteristics and the effect of different diversity objectives – suggesting that there is much to be gained from adapting diversity objectives to the specific problem being solved.