Solving spatio-temporal optimization problems with genetic algorithms: A case study of a bald cypress seed dispersal and establishment model

Spatial-temporal optimization problems (e.g., maximize the areal distribution of an outcome in a certain time period) are difficult to solve because they are typically not well formulated and their search space is often intractable. In this paper, by using a seed dispersal model of bald cypress as a case study, we illustrate the intractability associated with traditional optimization techniques when they are used to address such problems, and present a genetic algorithm (GA) approach that is designed to overcome these difficulties. We conclude by demonstrating the emergence of optimal or near optimal solutions that yield maximized distributions of cypress during the period of simulation