to the ABAGAIL engineers’ domain knowledge; the algorithm’s crossover function was specifically tailored to efficiently create ‘offspring’ of two paths. With such an efficient crossover function, the most efficient sub-paths of two parent paths could be merged multiple times through each generation, allowing the algorithm to quickly converge to an optimal solution. Ultimately, in problems like the Traveling Salesman problem where the search space is not well defined (for example, a random graph), Genetic Algorithms tend to be most effective. Figure 6. Traveling Salesman fitness results compared to optimization algorithm iterations. Flip Flop Problem – Simulated Annealing. The Flip Flop problem is, by far, the simplest optimization problem used throughout our analysis. At its core, Flip Flop involves a rudimentary fitness function which looks to find the total number of consecutive bit alternations within a bit string. In other words, while a bit string of ‘000’ would score 0, a bit string of ‘101’ would score 2. As the optimal configuration of bits within a bit string of length N would consist of continuously alternating bits, the global optima of such a problem would be exactly N – 1. Our goal is to determine which optimization algorithm performs best on this problem. Like before, we ran two experiments; one to observe an algorithm’s ability to scale to larger search spaces (increasing the size of the bit string), and another to determine optimization efficiency. We utilized ABAGAIL’s default hyperparameters in our testing, which can be seen in Table 5.>