In our GA, each individual in the p

In our GA, each individual in the population stores rewards for each of the eight actions that can be performed by the scheduler. Initially, these rewards are randomly generated. We evaluate our initial population by conducting execution-driven simulations with each individual’s memory scheduler configuration, using a small subset of our application set5 and determining the fitness of each individual. The fitness-based selection criteria that we use is tournament selection combined with elitist selection [23, 19]. To perform crossover, we randomly pick two individuals and swap the reward values of an action. Mutation is performed by randomly replacing the reward of an action with another value. Multiple-point crossover and mutations are performed in our experiments, which means that reward values can be swapped or replaced multiple times within a given individual. Once we have the population set for the next generation, it is evaluated against the fitness criteria, and this iterative evolutionary search process continues until we reach 50 generations, at the end of which we are left with a set of rewards, one per possible action, which together constitute our reward function. per possible action, which together constitute our reward function.