Genetic algorithms are an old and classic staple of LW. [1]
Genetic algorithms (as used in optimization problems) traditionally assume “full connectivity”, that is any two candidates can mate. In other words, population network is assumed to be complete and potential mate is randomly sampled from the population.
Aymeric Vié has a paper out showing (numerical experiments) that some less dense but low average shortest path length network structures appear to result in better optimization results: https://doi.org/10.1145/3449726.3463134
Maybe this isn’t news for you, but it is for me! Maybe it is not news to anyone familiar with mathematical evolutionary theory?
This might be relevant for any metaphors or thought experiments where you wish to invoke GAs.
Genetic algorithms are an old and classic staple of LW. [1]
Genetic algorithms (as used in optimization problems) traditionally assume “full connectivity”, that is any two candidates can mate. In other words, population network is assumed to be complete and potential mate is randomly sampled from the population.
Aymeric Vié has a paper out showing (numerical experiments) that some less dense but low average shortest path length network structures appear to result in better optimization results: https://doi.org/10.1145/3449726.3463134
Maybe this isn’t news for you, but it is for me! Maybe it is not news to anyone familiar with mathematical evolutionary theory?
This might be relevant for any metaphors or thought experiments where you wish to invoke GAs.
[1] https://www.lesswrong.com/search?terms=genetic%20algorithms