Searching among Search Spaces: Hastening the genetic evolution of feedforward neural networks

  • Vittorio Maniezzo


The paper introduces a genetic paradigm for evolving feedforward neural network, where both the network topology and the weights distribution are coded in the individuals. The resulting binary coded strings are too long for efficient evolution, thus two novel techniques are employed. The first consists in a coding procedure that allows the genetic algorithm to evolve the length of the coding string along with its content. This goes by the name of granularity evolution procedure. The second technique is inspired by linear programming and yields a fast and accurate fine tuning of the solutions.

These ideas have been implemented in a running system. Computational results show how during evolution the genetic algorithm uses several coding lengths, thus it autonomously identifies the search spaces that lead to more promising solutions.


Genetic Algorithm Search Space Feedforward Neural Network Mutation Probability Weight Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 1993

Authors and Affiliations

  • Vittorio Maniezzo
    • 1
  1. 1.Artificial Intelligence and Robotics Project Dipartimento di Elettronica e InformazionePolitecnico di MilanoItaly

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