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M2ICAL: A Tool for Analyzing Imperfect Comparison Algorithms | IEEE Conference Publication | IEEE Xplore

M2ICAL: A Tool for Analyzing Imperfect Comparison Algorithms


Abstract:

Practical optimization problems often have objective functions that cannot be easily calculated. As a result, comparison-based algorithms that solve such problems use com...Show More

Abstract:

Practical optimization problems often have objective functions that cannot be easily calculated. As a result, comparison-based algorithms that solve such problems use comparison functions that are imperfect (i.e. they may make errors). Machine learning algorithms that search for game-playing programs are typically imperfect comparison algorithms. This paper presents M2ICAL, an algorithm analysis tool that uses Monte Carlo simulations to derive a Markov chain model for imperfect comparison algorithms. Once an algorithm designer has modeled an algorithm using M2ICAL as a Markov chain, it can be analyzed using existing Markov chain theory. Information that can be extracted from the Markov chain include the estimated solution quality after a given number of iterations; the standard deviation of the solutions' quality; and the time to convergence.
Date of Conference: 29-31 October 2007
Date Added to IEEE Xplore: 04 January 2008
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Conference Location: Patras, Greece

References

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