A steady-State Genetic Algorithm with Resampling for Noisy Inventory Control

S. Prestwich, S.A. Tarim, R. Rossi, B. Hnich

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

6 Citations (Scopus)


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
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - PPSN X
EditorsG. Rudolph, Th. Jansen, S.M. Lucas, C. Poloni, N. Beume
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin/Heidelberg

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