Fitness Caching - From a Minor Mechanism to Major Consequences in Modern Evolutionary Computation

In the field of Evolutionary Computation, the main objective is to find a high-quality solution to the considered problem. However, the other important issue is to find a solution efficiently. Therefore, evolutionary methods use various techniques to adjust to the problem and reduce the amount of resources consumed during an optimization process. One of the well-known and relatively simple techniques is storing the already evaluated genotypes and their ratings. Whenever an evolutionary method is to evaluate the fitness for a genotype that was already rated, instead of re-evaluating it, a method may use the value that was stored in the repository. Surprisingly, despite its simplicity, such a fitness caching technique was shown to cause many important phenomena. When an evolutionary method is stuck, fitness caching may cause such significant fitness function evaluation (FFE) reduction that FFE will not be a reliable resource consumption measure anymore. Moreover, fitness caching may help in detecting the drop in the number of new solutions investigated by a method. Thus, it may help in dynamic population-size management. Such a consequence is far more sophisticated than a simple FFE reduction. Therefore, in this paper, we investigate fitness caching in more detail. We analyze its influence on chosen state-of-the-art methods employed to solve well-known theoretical problems.