A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Incorporating expert knowledge in evolutionary search: a study of seeding methods
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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In human genetics it is now possible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and disease processes it seems likely that disease risk can best be modeled by interactions between biological components, which may be examined as interacting DNA sequence variations. The machine learning challenge is to e.ectively explore interactions in these datasets to identify combinations of variations which are predictive of common human diseases. Genetic programming is a promising approach to this problem. The goal of this study is to examine the role that an expert knowledge aware initializer can play in the framework of genetic programming. We show that this expert knowledge aware initializer outperforms both a random initializer and an enumerative initializer.