A statistical comparison of grammatical evolution strategies in the domain of human genetics
作者： B.C. White , D.M. Reif , J.C. Gilbert , J.H. Moore
关键词: Domain (software engineering) 、 Genetic model 、 Search algorithm 、 Computer science 、 DNA 、 Artificial intelligence 、 DNA sequencing 、 Evolutionary computation 、 Random search 、 Grammatical evolution 、 Human genome 、 Swarm behaviour 、 Human genetics 、 Machine learning
摘要: Detecting and characterizing genetic predictors of human disease susceptibility is an important goal in genetics. New chip-based technologies are available that facilitate the measurement thousands DNA sequence variations across genome. Biologically-inspired stochastic search algorithms expected to play role analysis these high-dimensional datasets. We simulated datasets with up 6000 attributes using two different models statistically compared performance grammatical evolution, swarm, random for building symbolic discriminant functions. found no statistical difference among within this specific domain.
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