Author: Michael Yee
Date: 05:14:22 04/07/05
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Hi Anthony, I don't really understand your strong words, since there are at least a few examples I can think of where very different algorithms can co-exist. For example, in linear programming, there's the simplex method (moving from extreme point to extreme point in a discrete fashion) and there are interior point methods (starting from an interior point of the feasible region and using a nonlinear technique [newton step] to move toward the optimal solution). Both algorithms perform well in practice. And (perhaps more relevant to the chess discussion), for solving hard optimization problems there are some "standard" techniques (say, using branch and bound and using LP or other relaxations to get bounds) and some nonstandard ones (e.g., meta-heuristics). I'm pretty sure you're familiar with these already, but approaches like genetic algorithms, simulated annealing, tabu search, ant colonies, etc., all get their inspiration from natural systems that seem to be able to efficiently solve their particular optimization problems. So both math programming based approaches and nature-insipired approaches can be viable though very different. It's not clear to me why chess has only *one* family of good algorithms. Michael
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