TR+software available - finding global minima

[Orsier Bruno]

"Another hybrid algorithm for finding a global mimimum of MLP error functions"
Technical Report UNIGE-AI-95-6
Bruno ORSIER, CUI, University of Geneva --- orsier at cui.unige.ch

ABSTRACT:
  This report presents \pstar, a new global optimization method for
  training multilayered perceptrons. Instead of local minima, global minima of
  the error function are found. This new method is hybrid in the sense that it
  combines three very different optimization techniques: Random Line Search,
  Scaled Conjugate Gradient and a 1-dimensional minimization algorithm named
  P$^*$. The best points of each component are retained by the hybrid method:
  simplicity of Random Line Search, efficiency of Scaled Conjugate Gradient,
  efficiency and convergence toward a global minimum for P$^*$. \pstar\ is
  empirically shown to perform better or much better than three other 
  global random optimization methods and  a global deterministic
  optimization method. 

Retrieval: http://cuiwww.unige.ch/AI-group/staff/orsier.html

\pstar and its test problems are
available for users of the Stuttgart Neural Network Simulator.
See also http://cuiwww.unige.ch/AI-group/staff/orsier.html for details.

Best regards,
Bruno Orsier		     			    E-mail: orsier at cui.unige.ch
University of Geneva	  WWW:http://cuiwww.unige.ch/AI-group/staff/orsier.html