Prestwich S.Tarim S.A.Rossi R.Hnich B.2023-06-162023-06-162008354087699597835408769910302-9743https://doi.org/10.1007/978-3-540-87700-4_56https://hdl.handle.net/20.500.14365/3396Sonderforschungsbereich 'Computational Intelligence' (SFB 531);Deutsche Forschungsgemeinschaft (DFG);Gesellschaft fur Informatik (GI)10th International Conference on Parallel Problem Solving from Nature, PPSN X -- 13 September 2008 through 17 September 2008 -- Dortmund -- 74252Noisy 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.eninfo:eu-repo/semantics/openAccessAlgorithmsChromosomesDiesel enginesFunction evaluationGenetic algorithmsGenetic engineeringInventory controlPopulation statisticsSamplingAlgorithmsChromosomesGenetic algorithmsInventory controlEvolutionary computationsGenetic diversitiesInventory control problemsNoise distributionsNoisy fitness functionsRe samplingsRuntime parametersProblem solvingProblem solvingFitness functionsGenetic diversityInventory control problemsNoise distributionNumber of samplesResampling techniqueRun time parametersSteady-state genetic algorithmsConference Object10.1007/978-3-540-87700-4_562-s2.0-56449086193