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Synchronized Oriented Mutations Algorithm for Training Neural Controllers

  • Vincent Berenz
  • Kenji Suzuki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5507)

Abstract

Developing neural controllers for autonomous robotics is a tedious task as the desired state trajectory of the robot is very often not known in advance. This led to the large success of evolutionary algorithm in this field. In this paper we introduce SOMA (Synchronized Oriented Mutations Algorithm), which presents an alternative for rapidly minimizing the parameters characterizing a given individual. SOMA is characterized by its easy implementation and its flexibility: it can use any continuous fitness function and be applied to optimize neural network of diverse topologies using any kind of activation functions. Contrary to evolutionary approach, it is applied on a single individual rather than on a population. Because the procedure is very fast, it allows for rapid screening and selection of good candidates. In this paper, the efficiency of SOMA at training ordered connection feed forward networks on function modeling problem, classification problem and robotic controllers is investigated.

Keywords

Mutation Operator Output Neuron Feed Forward Network Input Neuron Iteration Maximum 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vincent Berenz
    • 1
  • Kenji Suzuki
    • 1
  1. 1.Artificial Intelligence Laboratory Department of Intelligent Interaction Technologies Graduate School of Systems and Information EngineeringUniversity of TsukubaJapan

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