Robotic Evolution: Complex Behaviours

I wrote a thesis that answers the question of whether the number of parameters a pathfinding algorithm takes directly correlates to its efficiency. Each pathfinding algorithm was tested using a simulation based on the game tag and was run 100 times to define each algorithm's average time to catch the runner. The different algorithms tested were random movement, Dijkstra's algorithm, A* algorithm and jump point search. The deliverables were able to show a strong correlation between efficiency and the number of parameters; as the parameters increased, the algorithm got better at catching the runner in the quickest number of cycles. The paper concludes that more parameters do equate to better efficiency when catching the runner within the simulation.