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Exploiting Overlap When Searching for Robust Optima

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Parallel Problem Solving from Nature, PPSN XI (PPSN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6238))

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Abstract

This paper introduces a new view on fitness evaluation when searching for robust optima. It proposes to compare solutions in (successive) populations with respect to how they rank in robustness instead of aiming for accurate robustness estimates. This can be done by focusing on the non-overlapping parts of the regions of uncertainty of each pair of candidate solutions. An initial step toward a scheme implementing this view is made with the analysis and experiments on a simple scenario comparing two solutions on uniform input noise.

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References

  1. Beyer, H.-G.: Towards a theory of ‘evolution strategies’. some asymptotical results from the (1,+lambda)-theory. Evolutionary Computation 1, 165–188 (1993)

    Article  Google Scholar 

  2. Beyer, H.-G.: Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering 186(2-4), 239–267 (2000)

    Article  MATH  Google Scholar 

  3. Beyer, H.-G., Sendhoff, B.: Evolution strategies for robust optimization. In: IEEE Congress on Evolutionary Computation (CEC 2006), pp. 1346–1353 (2006)

    Google Scholar 

  4. Beyer, H.-G., Sendhoff, B.: Robust optimization - a comprehensive survey. Computer Methods in Applied Mechanics and Engineering 196(33-34), 3190–3218 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  5. Branke, J.: Creating robust solutions by means of evolutionary algorithms. In: Parallel Problem Solving from Nature (PPSN V), pp. 119–128 (1998)

    Google Scholar 

  6. Branke, J.: Reducing the sampling variance when searching for robust solutions. In: Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 235–242 (2001)

    Google Scholar 

  7. Jin, Y., Branke, J.: Evolutionary optimizationin uncertain environments - a survey. IEEE Transaction on Evolutionary Computation 9(3), 303–317 (2005)

    Article  Google Scholar 

  8. Paenke, I., Branke, J., Jin, Y.: Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation 10(4), 405–420 (2006)

    Article  Google Scholar 

  9. Schwefel, H.-P.: Evolution and Optimum Seeking: The Sixth Generation. John Wiley & Sons, Inc., Chichester (1993)

    Google Scholar 

  10. Tsutsui, S.: A comparative study on the effects of adding perturbations to phenotypic parameters in genetic algorithms with a robust solution searching scheme. In: IEEE Systems, Man, and Cybernetics Conference (SMC 1999), pp. 585–591 (1999)

    Google Scholar 

  11. Tsutsui, S., Ghosh, A.: Genetic algorithms with a robust solution searching scheme. IEEE Transactions on Evolutionary Computation 1(3), 201–208 (1997)

    Article  Google Scholar 

  12. Tsutsui, S., Ghosh, A.: Effects of adding perturbations to phenotypic parameters in genetic algorithms for searching robust solutions. In: Advances in Evolutionary Computing: Theory and Applications, pp. 351–365 (2003)

    Google Scholar 

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Kruisselbrink, J., Emmerich, M., Deutz, A., Bäck, T. (2010). Exploiting Overlap When Searching for Robust Optima. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_7

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  • DOI: https://doi.org/10.1007/978-3-642-15844-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15843-8

  • Online ISBN: 978-3-642-15844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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