Epidemiology is a discipline that has a crucial role in describing health status, identifying risk factors, and analyzing relationships between health and different hazardous agents. Gulis and Fujino (2020)The classical epidemiological triangle of host-agent-environment describes how individuals become ill. Disease occurs when an outside agent (vector) capable of causing disease or injury meets a host that is vulnerable to the agent. This happens in an environment that allows the agent and host to interact. Epidemiology not only measures the relationships between hosts and agents in certain environments but also analyses the health status of the population living in that environment.
Upon running the grid search, it is clear that an initial temperature of 100,000 and cooling factor of 0.25 offer optimal performance of the model. Next, we generated a curve demonstrating accuracy of our model’s training and testing set classification over 1,000 iterations (Figure 2).
Figure 2. NN feed-forward accuracy over 1,000 iterations of the Simulated Annealing algorithm.
The curve generated for our Simulated Annealing model looks strikingly similar to that generated using Randomized Hill Climbing; the algorithm runs into the same delayed reaction and accuracy anomalies, though at different iterations. This can be attributed to the algorithm’s similarities to Randomized Hill Climbing, as well as evidence that interactions of the different attributes within the neural network’s hidden layer are largely responsible for the behavior.
Running 1,000 iterations of the Simulated Annealing algorithm lasted 19.56 seconds, placing the algorithm among the fastest within randomized optimization. Looking forward, the accuracy would likely converge higher if the algorithm given more iterations to run. Aside from this, the accuracy could be measured marginally higher by averaging results over multiple trials, or performing a grid search with smaller steps to find a more optimal hyperparameter combination. Ultimately, although achieving slightly higher accuracy than Randomized Hill Climbing, the Simulated Annealing algorithm still falls fall short of backpropagation impressive 97.38% testing accuracy.
Genetic Algorithm
The Genetic Algorithm applies biological principles to optimization problems; using a system of mating populations in order to find the fittest member of a search space. From a high level, the algorithm encodes relevant information of a search space into ‘chromosomes’ of population individuals, and randomly crosses-over these chromosomes over each generation using ratios encoded as constants. Through each generation, the best members are carried over and the worst are discarded. Over enough iterations, the population will contain optimal members of the search space.
Three important hyperparameters are necessary in order to fine-tune a Genetic Algorithm optimization model: the mutation count, or the number of individuals who have their chromosomes spontaneously mutated, the mating count, or the number of individuals who mate to produce offspring, and the population size, or the number of individuals to maintain each generation. Too many mutating individuals can prevent the algorithm from maintaining a genetically superior population. These mutations are crucial, however, as they prevent the algorithm from halting at local minima by applying randomness. As such, w
