Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary 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
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Initialization method for grammar-guided genetic programming
Knowledge-Based Systems
Tuning ReliefF for genome-wide genetic analysis
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Exploiting expert knowledge in genetic programming for genome-wide genetic analysis
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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For biomedical researchers it is now possible to measure large numbers of DNA sequence variations across the human genome. Measuring hundreds of thousands of variations is now routine, but single variations which consistently predict an individual's risk of common human disease have proven elusive. Instead of single variants determining the risk of common human diseases, it seems more likely that disease risk is best modeled by interactions between biological components. The evolutionary computing challenge now is to effectively explore interactions in these large datasets and identify combinations of variations which are robust predictors of common human diseases such as bladder cancer. One promising approach to this problem is genetic programming (GP). A GP approach for this problem will use darwinian inspired evolution to evolve programs which find and model attribute interactions which predict an individual's risk of common human diseases. The goal of this study is to develop and evaluate two initializers for this domain. We develop a probabilistic initializer which uses expert knowledge to select attributes and an enumerative initializer which maximizes attribute diversity in the generated population. We compare these initializers to a random initializer which displays no preference for attributes. We show that the expert-knowledge-aware probabilistic initializer significantly outperforms both the random initializer and the enumerative initializer.We discuss implications of these results for the design of GP strategies which are able to detect and characterize predictors of common human diseases.