Adaptation and Genomic Evolution in EcoSim

Artificial life evolutionary systems facilitate addressing lots of fundamental questions in evolutionary genetics. Behavioral adaptation requires long term evolution with continuous emergence of new traits, governed by natural selection. We model organism’s genomes coding for their behavioral model and represented by fuzzy cognitive maps (FCM), in an individual-based evolutionary ecosystem simulation (EcoSim). Our system allows the emergence of new traits and disappearing of others, throughout a course of evolution. In this paper we show how continuous adaptation to a changing environment affects genomic structure and genetic diversity. We adopted the notion of Shannon entropy as a measure of genetic diversity. We emphasized the difference in genetic diversity between EcoSim and its neutral model (a partially randomized version of EcoSim). In addition, we studied the effect that genetic diversity has on species fitness and we showed how they correlate with each other. We used Random Forest to build a classifier to further validate our findings, along with some meaningful rule extraction.