Internal reinforcement in a connectionist genetic programming approach

@article{Teller2000InternalRI,
  title={Internal reinforcement in a connectionist genetic programming approach},
  author={Astro Teller and M. Veloso},
  journal={Artif. Intell.},
  year={2000},
  volume={120},
  pages={165-198}
}
Abstract Genetic programming (GP) can learn complex concepts by searching for the target concept through evolution of a population of candidate hypothesis programs. However, unlike some learning techniques, such as Artificial Neural Networks (ANNs), GP does not have a principled procedure for changing parts of a learned structure based on that structure's performance on the training data. GP is missing a clear, locally optimal update procedure, the equivalent of gradient-descent backpropagation… Expand
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