Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters

Our latest paper, Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters, is now available at ScienceDirect and referenced through ACM. Here’s the abstract:

In this paper we present a comparative study of several methods that combine evolutionary algorithms and local search to optimize multilayer perceptrons: A method that optimizes the architecture and initial weights of multilayer perceptrons; another that searches for training algorithm parameters, and finally, a co-evolutionary algorithm, introduced here, that handles the architecture, the network’s initial weights and the training algorithm parameters. Our aim is to determine how the co-evolutive method can obtain better results from the point of view of running time and classification ability. Experimental results show that the co-evolutionary method obtains similar or better results than the other approaches, requiring far less training epochs and thus, reducing running time.

Just leave a comment here if you want a copy, or email us at jjmerelo (at) gmail (dot) com.

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