Random cost simulations were introduced as a method to investigate
optimization problems in systems with conflicting constraints. Here I study the
approach in connection with the training of a feed-forward multilayer
perceptron, as used in high energy physics applications. It is suggested to use
random cost simulations for generating a set of selected configurations. On
each of those final minimization may then be performed by a standard algorithm.
For the training example at hand many almost degenerate local minima are thus
found. Some effort is spend to discuss whether they lead to equivalent
classifications of the data.Comment: 16 pages and 8 figures. Typos in eqn.(1) and various misleading
formulations eliminate
