Digital image processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic programming and evolutionary generalization
IEEE Transactions on Evolutionary Computation
MOGE: GP classification problem decomposition using multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited in previous work. Here, we revisit the design of fitness functions for genetic programming by explicitly considering the contribution of the wrapper and cost function. Within the context of supervised learning, as applied to classification problems, a clustering methodology is introduced using cost functions which encourage maximization of separation between in and out of class exemplars. Through a series of empirical investigations of the nature of these functions, we demonstrate that classifier performance is much more dependable than previously the case under the genetic programming paradigm.