Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

The study of macaque monkeys harbors advancements in the field of biomedical research. It is imperative to understand the genetic composition of different species of macaques to assess their accuracy as non-human primate (NHP) models for disease detection and treatment assessments. We sought to characterize the hybridization and admixture of the Southeast Asian macaques using single nucleotide polymorphism markers and analyzing the populations on the mainland and the island. Using AMOVA tests and STRUCTURE analysis, we determined that there are three distinct populations: Macaca mulatta, M. fascicularis fascicularis, and M. f. aurea. Furthermore, the island species holds an isolated population of M. f. aurea that demonstrate high inbreeding and genetic uniqueness compared to the mainland species. Findings from this study confirm that NHP models may need to be modified or updated according to changing allelic frequencies and genetic drift.