Solving test case based problems with fuzzy dominance
Jason Zutty, Gregory Rohling · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
Genetic algorithms and genetic programming lend themselves well to the field of machine learning, which involves solving test case based problems. However, most traditional multi-objective selection methods work with scalar objectives, such as minimizing false negative and false positive rates, that are computed from underlying test cases.