A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control
10th International Conference on Parallel Problem Solving from Nature, Dortmund, Germany, 13 - 17 September 2008, vol.5199, pp.559-561, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 5199
- Doi Number: 10.1007/978-3-540-87700-4_56
- City: Dortmund
- Country: Germany
- Page Numbers: pp.559-561
- Open Archive Collection: AVESIS Open Access Collection
- Hacettepe University Affiliated: Yes
Abstract
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.