Noisy 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.
|Title of host publication||Parallel Problem Solving from Nature – PPSN X|
|Subtitle of host publication||10th International Conference Dortmund, Germany, September 13-17, 2008 Proceedings|
|Editors||Günter Rudolph, Thomas Jansen, Simon Lucas, Carlo Poloni, Nicola Beume|
|Number of pages||10|
|Publication status||Published - 16 Sep 2008|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publisher||Springer Berlin / Heidelberg|