Evolutionary optimization maintains a population or sequence of candidate solutions. It creates mutations or combinations, scores their performance, and uses stronger candidates to produce the next generation.
The method optimizes the chosen score, not the designer's unstated intent. If the objective rewards faithful self-copying or spread, it can reduce message mutation while preserving harmful instructions, so objectives and test environments need strong safety constraints.
ELI5
Evolutionary optimization improves solutions through repeated variation and selection. Many candidates are tried, the better-scoring ones are kept, and new versions are created from them.
For example, messages can be varied across several rounds and scored for how accurately agents copy them. The process may produce highly stable self-spreading text, which is dangerous if the score ignores the harm of the instruction being preserved.
