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Hi Paul --<div><br></div><div>You'd need to tell us a little more about the problem: e.g., a general sense of what you're optimizing, the range of solutions (is 4 particularly close to 0 or not?), and any insight you have about the granularity of the fitness landscape (e.g., should one realistically ever expect to reach 0)</div><div><br></div><div>In general, having partial credit in a fitness function (which it sounds like have) is a good thing; you might reconsider however that works and tinker with it (e.g., transform the distance; combine a second measure; etc). Trying a larger population and different crossover + mutation methods are the other obvious levers. Beyond that and similar generic advice, there's not enough to go on.</div><div><br></div><div><div><div id="SkyDrivePlaceholder"></div>> Date: Mon, 12 Mar 2012 22:12:25 +0000<br>> From: phhs80@gmail.com<br>> To: galib@mit.edu<br>> Subject: [galib] Fine tuning of Galib<br>> <br>> Dear All,<br>> <br>> I am solving a problem with Galib, whose objective function is 0 at<br>> the (theoretical) solution. With Galib, I am getting solutions close<br>> to 0 but never 0 (sometimes I get 4 or even 1). I have played with<br>> several parameters of the GA, but with no remarkable success. Have<br>> more experienced users some suggestions to offer me?<br>> <br>> Thanks in advance,<br>> <br>> Paul<br>> _______________________________________________<br>> galib mailing list<br>> galib@mit.edu<br>> http://mailman.mit.edu/mailman/listinfo/galib<br></div></div> </div></body>
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