Crossover Can Be Constructive When Computing Unique Input Output Sequences

1. Lehre, P.K., Yao, X.: Runtime analysis of (1+1) EA on computing unique input output sequences. In: Proceedings of 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pp. 1882–1889 (2007)

   Google Scholar 

2. Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines-a survey. Proceedings of the IEEE 84(8), 1090–1123 (1996)

   Article  Google Scholar 

3. Clark, J.A., Dolado, J.J., Harman, M., Hierons, R.M., Jones, B., Lumkin, M., Mitchell, B., Mancoridis, S., Rees, K., Roper, M., Shepperd, M.: Reformulating software engineering as a search problem. IEE Proceedings-Software 150(3), 161–175 (2003)

   Article  Google Scholar 

4. Derderian, K.A., Hierons, R.M., Harman, M., Guo, Q.: Automated unique input output sequence generation for conformance testing of fsms. The Computer Journal 49(3), 331–344 (2006)

   Article  Google Scholar 

5. Guo, Q., Hierons, R.M., Harman, M., Derderian, K.A.: Computing unique input/output sequences using genetic algorithms. In: Petrenko, A., Ulrich, A. (eds.) FATES 2003. LNCS, vol. 2931, pp. 164–177. Springer, Heidelberg (2004)

   Chapter  Google Scholar 

6. Guo, Q., Hierons, R.M., Harman, M., Derderian, K.A.: Constructing multiple unique input/output sequences using metaheuristic optimisation techniques. IEE Proceedings Software 152(3), 127–140 (2005)

   Article  Google Scholar 

7. Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276, 51–81 (2002)

   Article  MathSciNet  MATH  Google Scholar 

8. He, J., Yao, X.: A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing 3(1), 21–35 (2004)

   Article  MathSciNet  MATH  Google Scholar 

9. Jansen, T., Wegener, I.: Evolutionary algorithms - how to cope with plateaus of constant fitness and when to reject strings of the same fitness. IEEE Transactions on Evolutionary Computation 5(6), 589–599 (2001)

   Article  Google Scholar 

10. Lehre, P.K., Yao, X.: Crossover can be constructive when computing unique input output sequences. Technical Report (CSR-08-08), University of Birmingham, School of Computer Science (2008)

    Google Scholar 

11. Jansen, T., Wegener, I.: The analysis of evolutionary algorithms - a proof that crossover really can help. Algorithmica 34(1), 47–66 (2002)

    Article  MathSciNet  MATH  Google Scholar 

12. Storch, T., Wegener, I.: Real royal road functions for constant population size. Theoretical Computer Science 320(1), 123–134 (2004)

    Article  MathSciNet  MATH  Google Scholar 

13. Fischer, S., Wegener, I.: The one-dimensional ising model: Mutation versus recombination. Theoretical Computer Science 344(2-3), 208–225 (2005)

    Article  MathSciNet  MATH  Google Scholar 

14. Sudholt, D.: Crossover is provably essential for the ising model on trees. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), pp. 1161–1167 (2005)

    Google Scholar 

15. Oliveto, P., He, J., Yao, X.: Analysis of population-based evolutionary algorithms for the vertex cover problem. In: Proceedings of the IEEE World Congress on Computational Intelligence (WCCI 2008), Hong Kong, June 1-6 (2008)

    Google Scholar 

16. Hoeffding, W.: Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58(301), 13–30 (1963)

    Article  MathSciNet  MATH  Google Scholar 

17. Oliveto, P., Witt, C.: Simplified drift analysis for proving lower bounds in evolutionary computation. Technical Report Reihe CI, No. CI-247/08, SFB 531, Technische Universität Dortmund, Germany (2008)

    Google Scholar 

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