Learning and Evolution in neural networks
Jeff Clune, a computer scientist interested in artificial life, has a paper out about how tuning evolutionary pressure in a certain way evolves networks with a high modularity. Let’s debunk this.
- tuning evolutionary pressure: emphasize cost of connections. This makes intuitive sense for networks in which there is a physical distance to cover (like a railway network); there are arguments for applying to more abstract networks like genetic and metabolic pathways. In online social networks, however, there is no physical distance; creating a new link is very cheap; maintaining a live social connection, however, is typically costly – and even, if we are to take the Dunbar number argument seriously, constrained by a hard ceiling.
- evolves: Clune’s model starts from random neural networks that need to perform a certain task. They receive stimuli from eight inputs (think an 8-pixel retina) and evolve to answer whether a pattern of interest is present, based on the stimuli. Patterns are perceived by two blocks of 4 pixels each; those considered (exogenously) of interest are slightly different for the two blocks (dubbed “left” and “right”). Evolution happens by simulating networks that reproduce according to a fitness measure (more fit networks have more offspring) and with random mutation. Two alternative measures of fitness are considered: maximizing performance only (PA) and maximizing performance while minimizing connection costs (P&CC). Performance is measured against two tasks: determine whether a pattern is present in both the right and the left pixel clusters (L-AND-R); or whether it is present in either the right or the left pixel clusters (L-OR-R). Which patterns count as an object differs in the left and right halves of the retina. Notice that the task assigned to the networks is itself modular, as a partition between “right” and “left” pixels is postulated. The results of the paper carry through for nonmodular problems, albeit in attenuated form.
Source: Contrordine compagni
Recent Advances In Simulated Evolution And Learning (Advances in Natural Computation)
Book (World Scientific Pub Co Inc)
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