Sample Solution

It is clear that a mutation count of 36 and a mating count of 50 provide the highest accuracy. We will apply the values gathered as ratios compared to the population size, and use these ratios as we run a linear search on the optimal population size. As seen in Figure 3, a population size of 300 is optimal. The curve rises from a population size of 100 to 300 and then plateaus, indicating the population size becomes large enough to represent the majority of the hypothesis space. Next, we use these hyperparameters to generate a curve comparing accuracy to the number of genetic algorithm iterations (or rather, generations); the results can be seen on Figure 4.

Figure 3. NN feed-forward accuracy compared to the Genetic Algorithm’s population size. Using the hyperparameters gathered from Table 3.

Figure 4. NN feed-forward accuracy over 1,000 iterations of the Genetic Algorithm.

The Genetic Algorithm’s train/test curve is strikingly different to that of Random Hill Climbing and Simulated Annealing; during the first ~300 generations, the training and testing curves are very turbulent as the population mutates from baseline accuracy. Interestingly, upon reaching the 300th iteration, the algorithm remains fairly consistent around 90% accuracy. Genetic algorithms are not known to scale well to large search spaces [3]. In these cases where a search space contains many local minima, these algorithms frequently can halt before finding the global optima. The Phishing Websites data set likely matches this definition with its over 30 attributes; this could be the cause of the convergence at such a low accuracy.
With enough computing power, one could attempt to further optimize hyperparameters by running a three-dimensional grid search over the population size, mutation count and mating count, with smaller step sizes. However, as we utilize ratios for our mutation and mating rates, it is likely that a two-dime