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


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Prestwich S., TARIM Ş. A., Rossi R., HNİCH B.

10th International Conference on Parallel Problem Solving from Nature, Dortmund, Almanya, 13 - 17 Eylül 2008, cilt.5199, ss.559-561 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 5199
  • Doi Numarası: 10.1007/978-3-540-87700-4_56
  • Basıldığı Şehir: Dortmund
  • Basıldığı Ülke: Almanya
  • Sayfa Sayıları: ss.559-561
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Noisy fitness function 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 stedy-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 average. and was more effective than four other tested techniques.