A steady-state genetic algorithm with resampling for noisy inventory control

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

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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.
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN X
Subtitle of host publication10th International Conference Dortmund, Germany, September 13-17, 2008 Proceedings
EditorsGünter Rudolph, Thomas Jansen, Simon Lucas, Carlo Poloni, Nicola Beume
PublisherSpringer-Verlag GmbH
Pages559-568
Number of pages10
Volume5199 LNCS
ISBN (Print)978-3-540-87699-1
DOIs
Publication statusPublished - 16 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Berlin / Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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