Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-decomposition strategy

@article{Kumar1998MultiobjectiveGA,
  title={Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-decomposition strategy},
  author={Rajeev Kumar and Peter Rockett},
  journal={IEEE transactions on neural networks},
  year={1998},
  volume={9 5},
  pages={822-30}
}
In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical learning. Our approach of "learning-follows-decomposition" is a generic solution to complex high-dimensional problems where the input space is partitioned prior to the hierarchical neural domain instead of by competitive learning. In this technique, clusters are generated on the basis of fitness of purpose--that is, they are… CONTINUE READING

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