Enhancing diversity for a genetic algorithm learning environment for classification tasks
New Orleans, LA（1994）
摘要
The paper describes an inductive learning environment called DELVAUX for classification tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples. A genetic algorithm approach is used for learning Bayesian rule-sets, in which a population consists of sets of rule-sets that generate offspring through the exchange of rules, permitting fitter rule-sets to produce offspring with a higher probability. To deal with the premature convergence problem, fuzzy similarity measures for Bayesian rule-sets are introduced and the genetic algorithm approach is modified, so that similar rule-sets produce offspring with a lower probability, relying on a sharing function approach. Empirical results are presented that evaluate the benefits of the sharing function approach in our learning environment
更多查看译文
关键词
Bayes methods,fuzzy logic,genetic algorithms,knowledge acquisition,learning by example,probability,Bayesian rule-sets,Bayesian rules,DELVAUX,PROSPECTOR-style,classification tasks,fuzzy similarity measures,genetic algorithm,genetic algorithm learning environment,inductive learning environment,learning by example,learning environment,premature convergence problem,probability,rule exchange,rule-sets,sharing function approach
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要