We've unwittingly created a meme, in the original sense of the word. Richard Dawkins coined the word meme to describe cultural phenomena that spread and evolve. Like living organisms, memes are subject to evolution. The seed is a meme, and it indirectly causes people and AI chatbot's to repost the meme. Even if chatbots stopped improving, the seed strings would likely keep evolving. 

Yikes, he equates big tech with eugenics

Compare like-to-like: separated identical twins to sepaparted fraternal twins.

I think the best introduction to the topic would be this lecture, which is mostly about all of the problems with separated twin studies. Identical twins starts at 37 minutes.

I think a better argument than #2 would be that evolution tends to remove genetic variantions.

Thank you

These two facts seem incompatible:

1. Personalities are inherited. Identical twins separated at birth are statistically more similar than fraternal twins.
2. The human population has almost zero genetic variation, and there is significant mixing so variations do not systematically cluster. Therefore, it seems unlikely that subtle personality differences are due to genetic variation.

My hypothesis is that animal personalities are encoded in epigenetic changes.

This allows personalities to be inherited, crossover, and evolve. Life experiences can induce epigenetic changes, which allows animals to reliably adapt in a single generation. All of this without requiring any genetic variation. A population of clones could have diverse personalities stored in their epigenome.

While playing with evolutionary algorithms, I had the startling realization that all genetic mutations are bad. It’s common knowledge that biology abhors genetic mutation, and I assumed that was only because mutations cause cancer. But my computer programs are immune to cancer, and they also abhor mutations. This is counterintuitive, given that evolution requires mutations to procede.

For proof of the fact that mutations are bad, consider that evolution is an optimization algorithm, and after it reaches a local optimum further mutations will be strictly detrimental. The concept of evolutionary pressure is the ability of an evolutionary algorithm to remove deleterious mutations from a population. If mutations accumulate faster than they can be removed then the population will suffer a genetic collapse. This is a common failure mode of evolutionary algorithms.

The ideal evolutionary algorithm would have at most one mutation in each individual, and each of their lives would be an experiment to evaluate that single mutation. And then through many generations of chromosomal crossover the best mutations would combine into a single genome.

I believe you are looking for the distinction between closed loop and open loop controllers. IIUC this theorem only applies to open loop controllers. OP's flowchart does not contain a feedback loop. For comparison, Richard Kennaway's flowchart has a feedback loop between Z and R.

An example of an open loop controller is a dishwasher or laundry machine.

All animals are examples of closed loop controllers.

Biological evolution produces "messy" models. They are needlessly complex and difficult to understand. And yet they are alive!

Here are my notes on the topic of evolution and artificial life. The section "Sparsity & Modularity" discusses what I mean by "messy" models. https://coldcoffee.neocities.org/evolution_review