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


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

10th International Conference on Parallel Problem Solving from Nature, Dortmund, Germany, 13 - 17 September 2008, vol.5199, pp.559-561 identifier identifier

  • 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

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.