Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
Non-destructive Depth-Dependent Crossover for Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Support vector classification with nominal attributes
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
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Genetic programming (GP) has proved successful at evolving pattern classifiers and although the paradigm lends itself easily to continuous pattern attributes, incorporating categorical attributes is little studied. Here we construct two synthetic datasets specifically to investigate the use of categorical attributes in GP and consider two possible approaches: indicator variables and integer mapping. We conclude that for ordered attributes, integer mapping yields the lowest errors. For purely nominal attributes, indicator variables give the best misclassification errors.