Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested techniques. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Prestwich, S., Tarim, S. A., Rossi, R., & Hnich, B. (2008). A steady-state genetic algorithm with resampling for noisy inventory control. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5199 LNCS, pp. 559–568). https://doi.org/10.1007/978-3-540-87700-4_56
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