Implementation of a simple Single Objective Evolutionary Algorithmn for sinusoidal optimization function. Created for fulfillment of CSE 598: Bio-Inspired AI and Optimization. Please reference if you use, and switch up some hyperparameters for your own use case :).

A common structure was chosen for the Genetic Algorithm implemented. Specifically, evolution was conducted by following the cyclic execution of Population Generation, Intercourse, and Mutation. The generation of the next population was executed through a series of sub-procedures: fitting the current population, selecting the most optimal candidates for breeding, and iteratively generating children until the next generation was of the size as the initial generation. Some results of fitnesses from populations of specific generations shown below.