Date of Award
2014
Publication Type
Doctoral Thesis
Degree Name
Ph.D.
Department
Computer Science
Keywords
artificial life, machine learning, predator prey
Supervisor
Gras, Robin
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
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). The emergent of new traits (genes) in EcoSim is examined by studying their effect on individual's fitness and well being. We examine how the new traits are used to predict the value of fitness using machine learning techniques. A comparison between the genomic evolution of EcoSim and a neutral model (a randomized version of EcoSim) is examined focusing on their respective genomic diversity. In order to further emphasize the importance of genetic diversity to adaptation and thus the well being of individuals, we were encouraged to study the effect that genetic diversity has on fitness. EcoSim gives us the chance to study the relation between species genetic diversity and average species fitness without the limits in environmental conditions and time scales found in biological studies, but in highly variable environments and across evolutionary time. The ecological effects of predator removal and its consequence on prey behavior have been investigated widely. We investigated the effects of predation risk on prey energy allocation and fitness. Here the role of predator removal on the contemporary evolution of prey traits such as movement, reproduction and foraging was evaluated. Our study clearly shows that predation risk alone induces behavioural changes in prey which drastically affect population and community dynamics, A classification algorithm was used to demonstrate the difference between genomes belonging to prey co-evolving with predators and prey evolving in the absence of predation pressure. We argue that predator introductions to naive prey might be destabilizing if prey have evolved and adapted to the absence of predators. Our results suggest that both predator introduction and predator removal from an ecosystem have widespread effects on the survival and evolution of prey by altering their genomes and behaviour, even after relatively short time intervals.
Recommended Citation
Khater, Marwa Fouad, "genomic and behavioural evolution in the artificial ecosystem simulation EcoSim" (2014). Electronic Theses and Dissertations. 5204.
https://scholar.uwindsor.ca/etd/5204