Evolution of large feedforward networks
Lois Boggess · 2003
This research employs evolution to create large (500 hidden unit) multiple layer perceptrons which attempt to perform six-way classification in a noisy environment. The classification is of words in text, according to their part of speech. The most promising aspect of the evolutionary strategy is that of allowing the entire population to evolve briefly, then split into separate populations which evolve independently, interbreed in a merged large population, split into separately evolving populations, and so on. Although this approach can be simulated on a sequential machine, it is inherently parallel. The resulting classifiers typically sustain a diversified classification strategy, as evidenced by their confusion matrices, beyond the point at which sequential steady state evolution has converged.