Adzni Abdul Rahim, (2008) A parametric study on reproductive competence in auto-constructive artificial life. Masters thesis, Universiti Malaysia Sabah.
Auto-constructive artificial life is the study of biological phenomena in silico using computer simulations of digital organisms that are capable of self-reproduction. Although a number of advanced artificial life Simulators have been developed recently, very little Is known about how reproductive competence may be affected by parametric changes of evolutionary settings in auto-constructive artificial life. This thesis presents a systematic investigation of how different parametric changes can affect the self-reproduction capabilities of a collectively-intelligent flying swarm of simulated organisms. To achieve this objective, an auto-constructive artificial life Simulation was developed based on the Breve system. This system contains various parameters whose values can be changed to control the characters of the swarm at the Genetic, Organism and Environment levels. Observations are then made on how the collective swarm evolves and is affected by different parameter settings in terms of reproductive competence. Each level has four individual parameters and is simulated for 50 runs with 50 different seeds which were terminated at 6000 generations each. The reproductive competence was measured at the start of a particular point of evaluation where no new organism is injected by the system within 5500 generations continuously and all new offspring were autonomously produced through the swarm's reproductively capabilities itself. A total of 6000 evolutionary simulation runs were conducted. From the results, it was found that the individual parameters at the Environment level were most sensitive to parametric changes compared to parameters at the Organism and Genetic levels. Overall, the three individual parameters that had the greatest impact on the swarm's reproduction competence were Number of Feeders at the Environment level (58%), followed by lifetime at the Organism level (42%) and Maximum Random Code Size at the Genetic level (38%).
|Item Type:||Thesis (Masters)|
|Uncontrolled Keywords:||reproductive, artificial life, auto-constructive, Breve system, Environment level, parameter|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||SCHOOL > School of Engineering and Information Technology|
|Deposited By:||IR Admin|
|Deposited On:||06 Aug 2014 09:08|
|Last Modified:||06 Aug 2014 09:08|
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