The Emergence of New Genes in EcoSim and Its Effect on Fitness

Abstract

The emergence of complex adaptive traits and behaviors in artificial life systems requires long term evolution with continuous emergence governed by natural selection. We model organism's genomes in an individual-based evolutionary ecosystem simulation (EcoSim), with fuzzy cognitive maps (FCM) representing their behavioral traits. Our system allows for the emergence of new traits and disappearing of others, throughout a course of evolution. We show how EcoSim models evolution through the behavioral model of its individuals governed by natural selection. We validate our model by examining the effect, the emergence of new genes, has on individual's fitness. Machine learning tools showed great interest lately in modern biology, evolutionary genetics and bioinformatics domains. We use Random Forest classifier, which has been widely used lately due to its power of dealing with large number of attributes with high efficiency, to predict fitness value knowing only the values of new genes. Furthermore discovering meaningful rules behind the fitness prediction encouraged us to use a pre processing step of feature selection. The selected features were then used to deduce important rules using the JRip learner algorithm.