Unpacking and Understanding Evolutionary Algorithms

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7311)


Theoretical analysis of evolutionary algorithms (EAs) has made significant progresses in the last few years. There is an increased understanding of the computational time complexity of EAs on certain combinatorial optimisation problems. Complementary to the traditional time complexity analysis that focuses exclusively on the problem, e.g., the notion of NP-hardness, computational time complexity analysis of EAs emphasizes the relationship between algorithmic features and problem characteristics. The notion of EA-hardness tries to capture the essence of when and why a problem instance class is hard for what kind of EAs. Such an emphasis is motivated by the practical needs of insight and guidance for choosing different EAs for different problems. This chapter first introduces some basic concepts in analysing EAs. Then the impact of different components of an EA will be studied in depth, including selection, mutation, crossover, parameter setting, and interactions among them. Such theoretical analyses have revealed some interesting results, which might be counter-intuitive at the first sight. Finally, some future research directions of evolutionary computation will be discussed.


IEEE Transaction Evolutionary Algorithm Problem Size Evolutionary Computation Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 82–102 (1999)CrossRefGoogle Scholar
  2. 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)CrossRefzbMATHGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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.2011.2174983Google Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefzbMATHGoogle Scholar
  10. 10.
    Yao, X., Islam, M.M.: Evolving artificial neural network ensembles. IEEE Computational Intelligence Magazine 3, 31–42 (2008)Google Scholar
  11. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Chong, S.Y., Tino, P., Yao, X.: Measuring generalization performance in co-evolutionary learning. IEEE Transactions on Evolutionary Computation 12, 479–505 (2008)CrossRefGoogle Scholar
  13. 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)MathSciNetCrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    He, J., Yao, X.: Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence 127, 57–85 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Hajek, B.: Hitting time and occupation time bounds implied by drift analysis with applications. Adv. Appl. Probab. 14, 502–525 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 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. 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)MathSciNetCrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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.2010.01.031Google Scholar
  22. 22.
    He, J., Yao, X.: A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing 3, 21–35 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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.2011.02.016Google Scholar
  25. 25.
    Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines — a survey. Proceedings of the IEEE 84(8), 1090–1123 (1996)CrossRefGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 27.
    Lehre, P.K., Yao, X.: Crossover can be constructive when computing unique input-output sequences. Soft Computing 15, 1675–1687 (2011)CrossRefzbMATHGoogle Scholar
  28. 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.2011.2112665Google Scholar
  29. 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)CrossRefGoogle Scholar
  30. 30.
    Neumann, F., Witt, C.: Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Springer, Berlin (2010)CrossRefzbMATHGoogle Scholar
  31. 31.
    Auger, A., Doerr, B. (eds.): Theory of Randomized Search Heuristics: Foundations and Recent Developments. World Scientific, Singapore (2011)zbMATHGoogle Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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. 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. 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.2012.01.001Google Scholar
  36. 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. 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)CrossRefGoogle Scholar
  38. 38.
    Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178, 2985–2999 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Yang, Z., Tang, K., Yao, X.: Scalability of generalized adaptive differential evolution for large-scale continuous optimization. Soft Computing 15, 2141–2155 (2011)CrossRefGoogle Scholar
  40. 40.
    Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Transactions on Evolutionary Computation (2011), doi:10.1109/TEVC.2011.2112662Google Scholar
  41. 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)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xin Yao
    • 1
  1. 1.CERCIA, School of Computer ScienceUniversity of BirminghamEdgbastonUK

Personalised recommendations