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@InProceedings(GECCO05-fsneat,
        author="Shimon Whiteson and Peter Stone and Kenneth O.\ Stanley and Risto Miikkulainen and Nate Kohl",
        title="Automatic Feature Selection via Neuroevolution",
        booktitle="Proceedings of the Genetic and Evolutionary Computation Conference",
        month="June", year="2005",
        abstract={
   Feature selection is the process of finding the set of inputs to a
   machine learning algorithm that will yield the best performance.
   Developing a way to solve this problem automatically would make
   current machine learning methods much more useful.  Previous efforts
   to automate feature selection rely on expensive meta-learning or are
   applicable only when labeled training data is available.  This paper
   presents a novel method called FS-NEAT which extends the NEAT
   neuroevolution method to automatically determine an appropriate set
   of inputs for the networks it evolves.  By learning the network's
   inputs, topology, and weights simultaneously, FS-NEAT addresses the
   feature selection problem without relying on meta-learning or
   labeled data.  Initial experiments in an autonomous car racing
   simulation demonstrate that FS-NEAT can learn better and faster than
   regular NEAT.  In addition, the networks it evolves are smaller and
   require fewer inputs.  Furthermore, FS-NEAT's performance remains
   robust even as the feature selection task it faces is made
   increasingly difficult.
        },
)