A new metric for evaluating genetic optimization of neural networks

In recent years researchers have used genetic algorithm techniques to evolve neural network topologies. Although these researchers have had the same end result in mind (namely, the evolution of topologies that are better able to solve a particular problem), the approaches they used varied greatly. Random selection of a genome coding scheme can easily result in sub-optimal genetic performance, since the efficiency of different evolutionary operations depends on how they affect schemata being processed in the population. In addition, the computational complexity involved in creating and evaluating neural networks usually does not allow for repetition of genetic experiments under different genome coding. I present an evaluation method that uses schema theory to aid the design of genetic codings for NN topology optimization. Furthermore, this methodology can help determine optimal balances between different evolutionary operators depending on the characteristics of the coding scheme. The methodology is tested on two GA-NN hybrid systems: one for natural language processing, and another for robot navigation.