Redundancy and computational efficiency in Cartesian genetic programming

Cartesian Genetic Programming: Why No Bloat?

“…Conversely, there is no evolutionary pressure to decrease the program size if the current program size is much larger than required to solve a given task. It therefore appears that using large program sizes is not detrimental to CGP, in keeping with previous results [5] which show it is actually beneficial. A new theory is presented as to why this is the case.…”

Recurrent Cartesian Genetic Programming of Artificial Neural Networks

“…Overestimating the number of available nodes has shown to greatly aid evolution [45,63]; which is thought to heighten neutral genetic drift but could also be compensating for length bias [18,19]. The reason it is thought that such a simple evolutionary strategy is so effective for CGP is twofold.…”

“…This could be considered a drawback, but overestimating the required number of nodes has been shown to be highly beneficial for CGP [45]. Additionally it has been shown for CGPANNs that the choice of the number of nodes has a far lower impact on performance than the choice of topology for non-topology evolving NE methods [65].…”

Cartesian Genetic Programming for Image Processing

“…As a result there are nodes in the representation that have no effect on the output, a feature known in GP as 'neutrality'. This has been shown to be very useful (Miller and Smith, 2006) in the evolutionary process. Also, because the genotype encodes a graph, there can be reuse of nodes, which makes the representation distinct from a classically tree-based GP representation.…”