Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2007.78
Title: M2ICAL: A tool for analyzing imperfect comparison algorithms
Authors: Oon, W.-C.
Henz, M. 
Issue Date: 2007
Source: Oon, W.-C., Henz, M. (2007). M2ICAL: A tool for analyzing imperfect comparison algorithms. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI 1 : 28-35. ScholarBank@NUS Repository. https://doi.org/10.1109/ICTAI.2007.78
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 M 2ICAL, 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. © 2007 IEEE.
Source Title: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
URI: http://scholarbank.nus.edu.sg/handle/10635/40783
ISBN: 076953015X
ISSN: 10823409
DOI: 10.1109/ICTAI.2007.78
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