Abstract
Genetic evolutionary algorithms are effective and optimal test generation methods. However, the methods to select the algorithm parameters are often ad hoc, relying on empirical data. We used a Markov-based method to model the genetic evolutionary test generation process, parameterise the process characteristics, and derive analytical solutions for selecting the optimisation parameters. The method eliminates preliminary test generation calibration and experimentation effort needed to select these parameters, which are used in current practice.
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Buriol L.S., Hirsch M.J., Pardalos P.M., Querido T., Resende M.G.C., Ritt M.: A biased random-key genetic algorithm for road congestion minimization. Optim. Lett. 4(4), 619–633 (2010)
Pardalos P.M., Romeijn E.: Handbook of global optimization—Volume 2. In: Pardalos, P.M., Romeijn, E. (eds) Heuristic approaches, Kluwer, Dordrecht (2002)
Corno, F., Cumani, G., Reorda, M.S., Squillero, G.: Fully automatic test program generation for microprocessor cores. Design, Automation and Test in Europe (DATE2003), pp. 1006–1011. Munich (2003)
Cheng, A., Lim, C.C.: Multi-objective genetic algorithms for system-on-chips verification. In: Proceedings of First World Congress on Global Optimization in Engineering and Science (WCGO2009). Changsha, (2009)
Fogel D.B.: Evolutionary computation: toward a new philosophy of machine intelligence, 2nd edn. IEEE Press, New York (2000)
Nix A.E., Vose M.D.: Modeling genetic algorithms with Markov chains. Ann. Math. Artif. Intell. 5, 79–88 (1992)
Mao, C. Y., Hu, Y. H.: Convergence analyses of simulated evolution algorithms, design Automation of High Performance VLSI Systems (GLSV’694), pp. 30–33. Madison (1994)
Reaume D.J., Romeijn E.H., Smith R.L.: Implementing pure adaptive search for global optimization using Markov chain sampling. J. Glob. Optim. 20(1), 33–47 (2001)
Baritompa W., Bulger D.W., Wood G.R.: Generating functions and the performance of backtracking adaptive search. J. Glob. Optim. 37(2), 159–175 (2007)
Rechenberg I.: Evolution strategy: optimization of systems according to principles of the biological evolution. Frommann-Holzboog, Stuttgart (1973)
Grinstead C.M., Snell J.L.: Introduction to probability, 2nd edn. American Mathematical Society, Providence (2006)
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Cheng, A., Lim, CC. Markov modelling and parameterisation of genetic evolutionary test generations. J Glob Optim 51, 743–751 (2011). https://doi.org/10.1007/s10898-011-9682-5
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DOI: https://doi.org/10.1007/s10898-011-9682-5