A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control
10th International Conference on Parallel Problem Solving from Nature, Dortmund, Almanya, 13 - 17 Eylül 2008, cilt.5199, ss.559-561, (Tam Metin Bildiri)
- 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
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- 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.