Abstract
This paper discusses the Genetic Algorithms for Noisy Fitness functions (GANF), that is, the Genetic Algorithms (GAs) for optimization of fitness function having random fluctuation. First, promising applications of the GANF are examined. Considering practical applicability of the GANF, requirements for the GANF are clarified. Several methods of the GANF proposed so far are overviewed, and the genetic algorithm with memory-based fitness evaluation (MFEGA) proposed by Sano and the author is described more in detail. Further, possible extensions of the GANF are also discussed.
