Evaluation scheduling in noisy environments

This paper investigates the problem of scheduling a fixed number of evaluations for genetic algorithms in noisy environments. With a fixed number of evaluations there is a tradeoff between the time the population is allowed to evolve (that is, the number of generations), the size of the population, and the number of samples scheduled per individual in an effort to reduce the effects of noise. This paper focuses mostly on the balance between allocating evaluations to the evolutionary phase versus allocating evaluations to selecting the individual with the highest fitness from the final population (the “champion selection” phase). Several different algorithms for scheduling evaluations during the champion selection phase are compared using a common test function to see how often they find the optimal value. The best algorithm is enhanced to improve its running time. We find the optimal split between the evolutionary and champion selection phases for the selected test function and we examine the effect of varying other parameters such as number of generations (and hence population) and evaluations per individual.