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Select Topics in the Analysis of Evolutionary Algorithms

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Analyzing Evolutionary Algorithms

Part of the book series: Natural Computing Series ((NCS))

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Abstract

Now that we have a collection of analytical tools tailored for the analysis of evolutionary algorithms, we are ready to consider concrete evolutionary algorithms and derive results, gain insights, prove facts about their behavior and performance. Of course, what one finds interesting and worth investigating in evolutionary computation is a personal question that is a matter of taste and other circumstances. It is almost inevitable that some questions that a reader finds most interesting will not be considered here. Therefore, the most important purpose this chapter serves is to be an example of how evolutionary algorithms can be analyzed using the methods described in the previous chapter. We do this considering four different topics that cover four different aspects of evolutionary computation. This includes considering effects of specific variation crossovers, dipping into the topic of design of evolutionary algorithms, considering a specific variant of evolutionary computation that we have not covered before, and, finally, considering an example for an application of evolutionary algorithms in combinatorial optimization.

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References

  1. T. Bäck, D.B. Fogel, Z. Michalewicz (eds.), Handbook of Evolutionary Computation (IOP Publishing/Oxford University Press, Bristol/Oxford, 1997)

    MATH  Google Scholar 

  2. B. Doerr, T. Jansen, D. Sudholt, C. Winzen, C. Zarges, Mutation rate matters even when optimizing monotonic functions. Evol. Comput. (2013). doi:10.1162/EVCO_a_00055

    Google Scholar 

  3. S. Forrest, M. Mitchell, Relative building block fitness and the building block hypotheses, in Foundations of Genetic Algorithms 2 (FOGA 1993), Vail, ed. by L.D. Whitley (Morgan Kaufmann, San Francisco, 1993), pp. 198–226

    Google Scholar 

  4. S. Forrest, J.H. Holland, M. Mitchell, When will a genetic algorithm outperform hill climbing? in Advances in Neural Information Processing Systems, ed. by J.D. Cowan, G. Tesauro, J. Alspector (Morgan Kaufmann, San Francisco, 1994), pp. 51–58

    Google Scholar 

  5. D.W. Hillis, Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D 42, 228–234 (1990)

    Article  Google Scholar 

  6. T. Jansen, I. Wegener, On the analysis of evolutionary algorithms – a proof that crossover really can help, in Proceedings of the 7th Annual European Symposium on Algorithms (ESA 1999), Prague, ed. by J. Nesetril. Lecture Notes in Computer Science, vol. 1643 (Springer, Berlin, 1999), pp. 184–193

    Google Scholar 

  7. T. Jansen, I. Wegener, On the choice of the mutation probability for the (1 + 1) EA, in Proceedings of the 6th International Conference on Parallel Problem Solving from Nature (PPSN 2000), Paris, ed. by M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo-Guervos, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 1917 (Springer, Berlin, 2000), pp. 89–98

    Google Scholar 

  8. T. Jansen, I. Wegener, Real royal road functions – where crossover provably is essential, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), San Francisco, ed. by L. Spector, E. Goodman, A. Wu, W. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon, E. Burke (Morgan Kaufmann, San Francisco, 2001), pp. 1034–1041

    Google Scholar 

  9. T. Jansen, I. Wegener, On the analysis of evolutionary algorithms – a proof that crossover really can help. Algorithmica 34(1), 47–66 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. T. Jansen, I. Wegener, Real royal road functions – where crossover provably is essential. Discret. Appl. Math. 149, 111–125 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  11. T. Jansen, I. Wegener, On the analysis of a dynamic evolutionary algorithm. J. Discret. Algorithms 4(1), 181–199 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. T. Jansen, R.P. Wiegand, Exploring the explorative advantage of the cooperative coevolutionary (1 + 1) EA, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), Chicago, ed. by I.E. Cantu-Paz, J. Foster, K. Deb, L. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. Potter, A. Schultz, K.A. Dowsland, N. Jonoska, J. Miller. Lecture Notes in Computer Science, vol. 2723 (Springer, Berlin, 2003), pp. 310–321

    Google Scholar 

  13. T. Jansen, R.P. Wiegand, Sequential versus parallel cooperative coevolutionary (1 + 1) EAs, in Proceedings of the IEEE International Congress on Evolutionary Computation (CEC 2003), Canberra (IEEE, Piscataway, 2003), pp. 30–37

    Google Scholar 

  14. T. Jansen, R.P. Wiegand, The cooperative coevolutionary (1 + 1) EA. Evol. Comput. 12(4), 405–434 (2004)

    Article  Google Scholar 

  15. T. Jansen, D. Sudholt, Analysis of an asymmetric mutation operator. Evol. Comput. 18(1), 1–26 (2010)

    Article  Google Scholar 

  16. T. Jansen, P.S. Oliveto, C. Zarges, On the analysis of the immune-inspired B-cell algorithm for the vertex cover problem, in Proceedings of the 10th International Conference on Artificial Immune Systems (ICARIS 2011), Cambridge. Lecture Notes in Computer Science, vol. 6825 (Springer, Berlin, 2011), pp. 117–131

    Google Scholar 

  17. M. Mitchell, An Introduction to Genetic Algorithms (MIT, Cambridge, 1995)

    Google Scholar 

  18. M. Mitchell, S. Forrest, Royal road functions, in Handbook of Evolutionary Computation, ed. by T. Bäck, D.B. Fogel, Z. Michalewicz (IOP Publishing/Oxford University Press, Bristol/Oxford, 1997), pp. B2.7:20–B.2.7:25

    Google Scholar 

  19. M. Mitchell, S. Forrest, J.H. Holland, The royal road function for genetic algorithms: fitness landscapes and GA performance, in Proceedings of the 1st European Conference on Artificial Life, Paris, ed. by F.J. Varela, P. Bourgine (MIT, Cambridge, 1992), pp. 245–254

    Google Scholar 

  20. F. Neumann, I. Wegener, Randomized local search, evolutionary algorithms, and the minimum spanning tree problem, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004), Seattle. Lecture Notes in Computer Science, vol. 3102 (Springer, Berlin, 2004), pp. 713–724

    Google Scholar 

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

    MATH  Google Scholar 

  22. M. Potter, K.A. De Jong, The coevolution of antibodies for concept learning, in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN 1998), Amsterdam, ed. by A. Eiben, T. Bäck, M. Schoenauer, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 1498 (Springer, Berlin, 1998), pp. 530–539

    Google Scholar 

  23. M. Potter, K.A. De Jong, Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)

    Article  Google Scholar 

  24. F. Rothlauf, Representations for Genetic and Evolutionary Algorithms (Springer, Berlin, 2006)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University College Cork, Cork, Ireland

    Thomas Jansen

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  1. Thomas Jansen
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Jansen, T. (2013). Select Topics in the Analysis of Evolutionary Algorithms. In: Analyzing Evolutionary Algorithms. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17339-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-17339-4_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17338-7

  • Online ISBN: 978-3-642-17339-4

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