Abstract
Excitement about your Disney park visit can be easily overshadowed by long lines, nauseous rides, and long days filled with unsatisfied customers around you. Why not employ genetic algorithms - nature's answer to process scheduling and trip optimizations. We discuss GA implementation with variable length chromosomes dynamically created based on the user's input regarding their park visit preferences (length of stay, waiting time, level of nausea, number of rides, etc). We have also accounted for speed-passes, which generates varied initial population. Aside from the initial user preferences, the fitness function takes into account walking distance between rides, whether a trip is child friendly, and preference of a certain type of ride among others. The final solution represents the optimized schedule of rides for the user as well as a few options in case the user has slight shift in preferences. Our experiments are very successful based on a pool of 50 user evaluations of predicted and actual experience. (C) 2014 The Authors. Published by Elsevier B.V.