Beyond memetic algorithms

I used to be a genetic algorithms enthusiast - but more recently I have come around to the idea that they are based on an old and out-of-date type of evolution - one that doesn't learn from its mistakes very efficiently. In the real world, genes invented brains that then led to culture and cultural evolution - which is a faster and better kind of evolution, one which is better able to approximate our ideals of inductive inference.

In this case, it seems sensible to follow nature's approach - and use virtual genes to control the parameters of neural networks (or a similar learning component) which are able to transmit their learned findings to each other via culture, bypassing their original genome. This is what is done in memetic algorithms.

Will nature go beyond this approach, and if so, can we help it along? One of the next steps seems fairly obvious: use a neural network (or a similar learning component) to help along the evolution of the virtual genes that control its own development by using intelligent design. In other words, use memetic and genetic engineering. This step finally closes the loop between brains and genes: first genes made brains and then brains learned how to do engineering and then became able to apply engineering techniques to their own genes.

There should be several benefits associated with using engineering techniques and intelligent design. Agents using self-directed evolution can select mutations deliberately, based on whether tests on them are likely to increase the knowledge base or otherwise lead to success. This eliminates some of the cost associated with testing random variants. Evaluation can be performed partly under simulation - which should be faster and cheaper than testing everything in real life. Interpretation of the results also changes as a result of being able to store a history of past successes and failures which can be processed using interpolation and extrapolation techniques in the development of new variants.