Automatically Choosing the Number of Fitness Cases: The Rational Allocation of Trials
For many problems to which genetic programming has been applied, choosing the number of tness cases with which to evaluate the individuals is a crucial decision. If too few tness cases are used, overrtting may occur, and the measured tness of an individual may not be representative of its true tness. On the other hand, if too many tness cases are used, a great deal of computer time can be wasted. This paper presents a method for the Rational Allocation of Trials (RAT) that dynamically allocates a boundedly optimal number of tness cases for each individual. RAT allocates individuals to tournaments prior to their evaluation, and then, borrowing from previous work in model selection, allocates trials ((tness cases) only to those individuals for whom the cost of evaluating another tness case is outweighed by the expected utility that the new information will provide. For most evolutionary computation approaches, including genetic programming, and for most problems, the RAT algorithm will provide signiicant time savings at minimal additional system complexity.