@inbook{5165d51c9bd44f4a882f6743b364ecfc,
title = "A steady-State Genetic Algorithm with Resampling for Noisy Inventory Control",
abstract = "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",
author = "S. Prestwich and S.A. Tarim and R. Rossi and B. Hnich",
year = "2008",
doi = "10.1007/978-3-540-87700-4_56",
language = "English",
isbn = "9783540876991",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin/Heidelberg",
number = "5199",
pages = "559--568",
editor = "G. Rudolph and Th. Jansen and S.M. Lucas and C. Poloni and N. Beume",
booktitle = "Parallel Problem Solving from Nature - PPSN X",
}