abstract = "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.",
notes = "GECCO-2006 A joint meeting of the fifteenth
international conference on genetic algorithms
(ICGA-2006) and the eleventh annual genetic programming
conference (GP-2006).