Not sure about this idea but if you did it there would be no reason to select only one algorithm necessarily or evolve only one. You could use *all* your algorithms up to that point and perhaps use the verdicts on each of them to weight the contribution from each algorithm.
<br><br>Might end up being computationally expensive but I believe MSoft are doing something a bit similar for live with about 200 or so different features including standard "features" like tf/idf, and page quality, all combined in a neural network. Somehow. I have no details sorry.
<br><br>-Ed<br><br><div><span class="gmail_quote">On 5/1/07, <b class="gmail_sendername">Aerik Sylvan</b> <<a href="mailto:aerik@thesylvans.com">aerik@thesylvans.com</a>> wrote:</span><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Okay, it's 10:01am in Silicon Valley and I'm awake and even reasonably alert :-)<br><br>Here's something I don't know if I've mentioned before: One thing we could do to apply the community to improve search results is to constantly rotate in different algorithms and let users rate the result relevance. Sure, maybe Joe Averageuser wouldn't want to have much to do with that, but even a dedicated core of few hundred power users could quickly create significant statistics about what algorithms are *actually* working better. But you'd never want to stop - constantly take in new ideas about what data to collect and how to weight it, then collect data about how well the algorithm is working in real time. Use the feedback from that to "evolve" the algorithms.
<br><br>(Cross-posting this to Brainstorming as well).<br><span class="sg"><br>Aerik<br>
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