I'd just keep a long list of high scorers for regression and occasionally reset the high score to zero. You can add random specimens to the population as well...
On 9/7/08, Benjamin Johnston <[EMAIL PROTECTED]> wrote: > > > Hi, > > > > I have a general question for those (such as Novamente) working on AGI > systems that use genetic algorithms as part of their search strategy. > > > > A GA researcher recently explained to me some of his experiments in > embedding prior knowledge into systems. For example, when attempting to > automate the discovery of models of a mechanical system, they tried adding > some "textbook models" to the set of genetic operators. The results weren't > good - the prior knowledge worked too well, causing the GA to converge too > fast onto the prior knowledge. so fast that there wasn't time for the GA to > build up sufficient diversity and quality in other solutions that might have > helped get out of the local maxima. The message seemed to be that prior > knowledge is too powerful - it can 'blind' a search - and that if you must > use it, you'd have to very very aggressively artificially deflate the > fitness of instances that use prior knowledge (and this is tricky to get > right). > > > > This struck me as relevant to GA-based AGIs that continually build on and > improve a knowledge-base. Once an AGI learns very simple initial models of > the world, if it then tries to evolve deeper knowledge about more difficult > problems (but, in the context of its prior learning), then its initial > models may prove to be too good: forcing the GA to converge on poor local > maxima that represent only minor variations on the initial models it learnt > in its earliest days. > > > > Does this issue actually crop up in GA-based AGI work? If so, how did you > get around it? If not, would you have any comments about what makes AGI > special so that this doesn't happen? > > > > -Ben > > > > > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
