A comparison of matrix rewriting versus direct encoding for evolving neural networks

The intuitive expectation is that the scheme used to encode the neural network in the chromosome should be critical to the success of evolving neural networks to solve difficult problems. In 1990 Kitano published an encoding scheme based on context-free parallel matrix rewriting. The method allowed compact, finite, chromosomes to grow neural networks of potentially infinite size. Results were presented that demonstrated superior evolutionary properties of the matrix rewriting method compared to a simple direct encoding. The authors present results that contradict those findings, and demonstrate that a genetic algorithm (GA) using a direct encoding can find good individuals just as efficiently as a GA using matrix rewriting