【論】Deutsch,2003,Evolutionary algorithms for find～

J. M. Deutsch
Evolutionary algorithms for finding optimal gene sets in microarray prediction
Bioinformatics Vol. 19 no. 1 2003
[PDF][Web Site]

・遺伝子抽出法、GESSES (genetic evolution of sub-sets of expressed sequences) の紹介。遺伝子の集合からランダムに遺伝子を足したり引いたりしつつ、評価関数（LOOCVに類似）に基づいてクラス識別に最適な遺伝子の集合を探索する。
・データ
1.SRBCT (Small round blue cell tumors) Data [Khan]
2.Leukemia Data [Golub]
3.DLBCL (Diffuse large B-cell lymphoma) Data [Shipp]

・結果「we were able to reduce the number of genes needed from 96 to less than 15, while at the same time being able to classify all of their test data perfectly.」
・原理「To determine which predictors are most successful, we utilize a scoring function which gives higher scores when more data points are correctly classified, that is the smallest classification error.」