Unpacking and Understanding Evolutionary Algorithms

1. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 82–102 (1999)

   Article  Google Scholar 

2. Li, B., Lin, J., Yao, X.: A novel evolutionary algorithm for determining unified creep damage constitutive equations. International Journal of Mechanical Sciences 44(5), 987–1002 (2002)

   Article  MATH  Google Scholar 

3. Yang, Z., Tang, K., Yao, X.: Self-adaptive differential evolution with neighborhood search. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC 2008), pp. 1110–1116. IEEE Press, Piscataway (2008)

   Chapter  Google Scholar 

4. Yang, Z., Li, X., Bowers, C., Schnier, T., Tang, K., Yao, X.: An efficient evolutionary approach to parameter identification in a building thermal model. IEEE Transactions on Systems, Man, and Cybernetics — Part C (2012), doi:10.1109/TSMCC.2011.2174983

   Google Scholar 

5. Tang, K., Mei, Y., Yao, X.: Memetic algorithm with extended neighborhood search for capacitated arc routing problems. IEEE Transactions on Evolutionary Computation 13, 1151–1166 (2009)

   Article  Google Scholar 

6. Handa, H., Chapman, L., Yao, X.: Robust route optimisation for gritting/salting trucks: A CERCIA experience. IEEE Computational Intelligence Magazine 1, 6–9 (2006)

   Article  Google Scholar 

7. Praditwong, K., Harman, M., Yao, X.: Software module clustering as a multi-objective search problem. IEEE Transactions on Software Engineering 37, 264–282 (2011)

   Article  Google Scholar 

8. Wang, Z., Tang, K., Yao, X.: Multi-objective approaches to optimal testing resource allocation in modular software systems. IEEE Transactions on Reliability 59, 563–575 (2010)

   Article  Google Scholar 

9. Dam, H.H., Abbass, H.A., Lokan, C., Yao, X.: Neural-based learning classifier systems. IEEE Transactions on Knowledge and Data Engineering 20, 26–39 (2008)

   Article  MATH  Google Scholar 

10. Yao, X., Islam, M.M.: Evolving artificial neural network ensembles. IEEE Computational Intelligence Magazine 3, 31–42 (2008)

    Google Scholar 

11. Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)

    Article  MathSciNet  MATH  Google Scholar 

12. Chong, S.Y., Tino, P., Yao, X.: Measuring generalization performance in co-evolutionary learning. IEEE Transactions on Evolutionary Computation 12, 479–505 (2008)

    Article  Google Scholar 

13. Salcedo-Sanz, S., Cruz-Roldán, F., Heneghan, C., Yao, X.: Evolutionary design of digital filters with application to sub-band coding and data transmission. IEEE Transactions on Signal Processing 55, 1193–1203 (2007)

    Article  MathSciNet  Google Scholar 

14. Zhang, P., Yao, X., Jia, L., Sendhoff, B., Schnier, T.: Target shape design optimization by evolving splines. In: Proc. of the 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pp. 2009–2016. IEEE Press, Piscataway (2007)

    Chapter  Google Scholar 

15. Li, Y., Hu, C., Yao, X.: Innovative batik design with an interactive evolutionary art system. J. of Computer Sci. and Tech. 24(6), 1035–1047 (2009)

    Article  Google Scholar 

16. He, J., Yao, X.: Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence 127, 57–85 (2001)

    Article  MathSciNet  MATH  Google Scholar 

17. Hajek, B.: Hitting time and occupation time bounds implied by drift analysis with applications. Adv. Appl. Probab. 14, 502–525 (1982)

    Article  MathSciNet  MATH  Google Scholar 

18. He, J., Yao, X.: Maximum cardinality matching by evolutionary algorithms. In: Proceedings of the 2002 UK Workshop on Computational Intelligence (UKCI 2002), Birmingham, UK, pp. 53–60 (September 2002)

    Google Scholar 

19. He, J., Yao, X.: Time complexity analysis of an evolutionary algorithm for finding nearly maximum cardinality matching. J. of Computer Sci. and Tech. 19, 450–458 (2004)

    Article  MathSciNet  Google Scholar 

20. Oliveto, P., He, J., Yao, X.: Analysis of the (1+1)-ea for finding approximate solutions to vertex cover problems. IEEE Transactions on Evolutionary Computation 13, 1006–1029 (2009)

    Article  Google Scholar 

21. Lehre, P.K., Yao, X.: Runtime analysis of the (1+1) ea on computing unique input output sequences. Information Sciences (2010), doi:10.1016/j.ins.2010.01.031

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

23. He, J., Yao, X.: From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6, 495–511 (2002)

    Article  Google Scholar 

24. Chen, T., Tang, K., Chen, G., Yao, X.: A large population size can be unhelpful in evolutionary algorithms. Theoretical Computer Science (2011), doi:10.1016/j.tcs.2011.02.016

    Google Scholar 

25. 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 

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

    Chapter  Google Scholar 

27. Lehre, P.K., Yao, X.: Crossover can be constructive when computing unique input-output sequences. Soft Computing 15, 1675–1687 (2011)

    Article  MATH  Google Scholar 

28. Lehre, P.K., Yao, X.: On the impact of mutation-selection balance on the runtime of evolutionary algorithms. IEEE Transactions on Evolutionary Computation (2011), doi:10.1109/TEVC.2011.2112665

    Google Scholar 

29. Chen, T., Tang, K., Chen, G., Yao, X.: Analysis of computational time of simple estimation of distribution algorithms. IEEE Transactions on Evolutionary Computation 14, 1–22 (2010)

    Article  Google Scholar 

30. Neumann, F., Witt, C.: Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Springer, Berlin (2010)

    Book  MATH  Google Scholar 

31. Auger, A., Doerr, B. (eds.): Theory of Randomized Search Heuristics: Foundations and Recent Developments. World Scientific, Singapore (2011)

    MATH  Google Scholar 

32. Chen, T., Lehre, P.K., Tang, K., Yao, X.: When is an estimation of distribution algorithm better than an evolutionary algorithm? In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 1470–1477. IEEE Press, Piscataway (2009)

    Chapter  Google Scholar 

33. Droste, S.: Analysis of the (1+1) ea for a dynamically changing onemax-variant. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, pp. 55–60. IEEE Press, Piscataway (2002)

    Google Scholar 

34. Rohlfshagen, P., Lehre, P.K., Yao, X.: Dynamic evolutionary optimisation: An analysis of frequency and magnitude of change. In: Proceedings of the 2009 Genetic and Evolutionary Computation Conference, pp. 1713–1720. ACM Press, New York (2009)

    Google Scholar 

35. Yu, Y., Yao, X., Zhou, Z.-H.: On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence (2012), doi:10.1016/j.artint.2012.01.001

    Google Scholar 

36. Fukunaga, A.S.: Genetic algorithm portfolios. In: Proceedings of the 2000 IEEE Congress on Evolutionary Computation, pp. 16–19. IEEE Press, Piscataway (2000)

    Google Scholar 

37. Peng, F., Tang, K., Chen, G., Yao, X.: Population-based algorithm portfolios for numerical optimization. IEEE Transactions on Evolutionary Computation 14, 782–800 (2010)

    Article  Google Scholar 

38. Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178, 2985–2999 (2008)

    Article  MathSciNet  MATH  Google Scholar 

39. Yang, Z., Tang, K., Yao, X.: Scalability of generalized adaptive differential evolution for large-scale continuous optimization. Soft Computing 15, 2141–2155 (2011)

    Article  Google Scholar 

40. Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Transactions on Evolutionary Computation (2011), doi:10.1109/TEVC.2011.2112662

    Google Scholar 

41. Blum, L., Shub, M., Smale, S.: On a theory of computation and complexity over the real numbers: NP-completeness, recursive functions and universal machines. Bulletin of the American Mathematical Society 21, 1–46 (1989)

    Article  MathSciNet  MATH  Google Scholar 

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