Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Classification Using Genetic Algorithms with a Novel Encoding
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genomic mining for complex disease traits with "random chemistry"
Genetic Programming and Evolvable Machines
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
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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We present a real-world application utilizing a Genetic Algorithm (GA) for exploratory multivariate association analysis of a large consumer survey designed to assess potential consumer adoption of Plug-in Hybrid Electric Vehicles (PHEVs). The GA utilizes an intersection/union crossover operator, in conjunction with high background mutation rates, to achieve rapid multivariate feature selection. We experimented with two alternative fitness measures based on classification results of a naïve Bayes quadratic discriminant analysis; one fitness function rewarded only for correct classifications, and the other penalized for the degree of misclassification using a quadratic penalty function. We achieved high classification accuracy for three different survey outcome questions (with 3-, 5-, and 7- outcome classes, respectively). The quadratic penalty function yielded better overall results, returning smaller feature sets and overall more accurate contingency tables of predicted classes. Our results help to identify what consumer attributes best predict their likelihood of purchasing a PHEV. These findings will be used to better inform an existing agent-based model of PHEV market penetration, with the ultimate aim of helping auto manufacturers and policy makers identify leverage points in the system that will encourage PHEV market adoption.