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Biological and ecological processes are complex systems formed by the complex interactions between biotic and abiotic components. Studies of these processes are difficult and in some cases infeasible due to these intricate relations. Individuals-Based Models (IBM) are virtual frameworks for simulating complex dynamical systems used successfully in studying natural phenomena.
EcoSim is an IBM for a predator-prey ecosystem simulation which includes an evolving behavioural model for the individuals. This model allows us to investigate different aspects of life by evolving individuals in a multi-level food chain simulation. In this model, a species is defined by a set of individuals with similar genomes.
This thesis studies two biological problems using EcoSim: Mate Choice and Species Extinction. The ornaments are the developed features for choosing a mate. This study compares three mate choice models: random, similar and exaggerated ornament according to the energy level as a strength indicator. Furthermore, this study investigates the effect of demographic, genetic and environmental factors on the possible future extinction of a species. These factors impact on a durability and lifespan of a species. Machine learning techniques are used to find the proper rules showing the differences in mate choice models and predicting the imminent extinction. The experiment results show several promising and interesting rules for each problem.