Abstract
Some authors claim that reporting the best result obtained by a stochastic algorithm in a number of runs is more meaningful than reporting some central statistic. In this short note, we analyze and refute the main argument brought in favor of this statement.
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Eiben, A.E., Jelasity, M.: A critical note on experimental research methodology in EC. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC’2002), pp. 582–587. IEEE, New York (2002)
Eiben A.E., Smith J.E. (2003). Introduction to Evolutionary Computing. Springer, Berlin Heidelberg New York
Fleming P.J., Wallace J.J. (1986). How not to lie with statistics: the correct way to summarize benchmark results. Commun. ACM 29(3):218–221
Barr R.S., Golden B.L., Kelly J.P., Resende M.G.C., Stewart W.R. (1995). Designing and reporting computational experiments with heuristic methods. J. Heuristics 1(1):9–32
Hooker J.N. (1995). Testing heuristics: We have it all wrong. J. Heuristics 1(1):33–42
Rardin R.R., Uzsoy R. (2001). Experimental evaluation of heuristic optimization algorithms: a tutorial. J. Heuristics 7(2):261–304
Stützle, T.G.: Local Search Algorithms for Combinatorial Problems – Analysis, Algorithms, and New Applications. PhD Thesis, Technische Universität Darmstadt (1999)
Glover F., Kochenberger G. (eds) (2002). Handbook of Metaheuristics. Kluwer, Norwell
Hoos H.H., Stützle T. (2004). Stochastic Local Search. Foundations and Applications. Morgan Kaufmann, San Francisco
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Birattari, M., Dorigo, M. How to assess and report the performance of a stochastic algorithm on a benchmark problem: mean or best result on a number of runs?. Optimization Letters 1, 309–311 (2007). https://doi.org/10.1007/s11590-006-0011-8
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DOI: https://doi.org/10.1007/s11590-006-0011-8