Evolutionary Design of Neural Network Architectures Using a Descriptive Encoding Language

Evolutionary algorithms are a promising approach to the automated design of artificial neural networks, but they require a compact and efficient genetic encoding scheme to represent repetitive and recurrent modules in networks. We present a problem-independent approach based on a human-readable and writable descriptive encoding using a high-level language. This encoding is based on developmental methods and a modular neural network paradigm. Here, we show that our approach works effectively by demonstrating that it can specify the search space compactly for "n-partition problems" and for sequence generation problems requiring recurrent networks, and that the evolved neural networks are parsimonious, modular, and capable of high-performance. We conclude that this approach based on high-level descriptive encoding can be useful in designing hierarchical, modular networks which may have recurrent connectivity, and is effective in describing the evolutionary search space, as well as the final neural networks resulting from the evolutionary process