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Analysis of a repair mechanism for the (1,λ)-ES applied to a simple constrained problem

Published:12 July 2011Publication History

ABSTRACT

We study the behaviour of a (1,λ)-ES that applies a simple repair mechanism to infeasible candidate solutions for the problem of maximising a linear function with a single linear constraint. Integral expressions that describe the strategy's one-generation behaviour are derived and used in a simple zeroth order model for the steady state of the strategy. Applied to the analysis of cumulative step size adaptation, the approach provides an intuitive explanation for the algorithm's behaviour as well as a condition on the setting of its parameters. A comparison with the strategy that resamples infeasible candidate solutions rather than repairing them is drawn, and the qualitatively different behaviour is explained.

References

  1. D. V. Arnold. Noisy Optimization with Evolution Strategies. Kluwer Academic Publishers, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. V. Arnold. On the use of evolution strategies for optimising certain positive definite quadratic forms. In Genetic and Evolutionary Computation Conference -- GECCO 2007, pages 634--641. ACM Press, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. V. Arnold. On the behaviour of the (1,lambda)-ES for a simple constrained problem. In H.-G. Beyer and W. B. Langdon, editors, Foundations of Genetic Algorithms -- FOGA 2011, pages 15--24. ACM Press, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. V. Arnold and H.-G. Beyer. Performance analysis of evolutionary optimization with cumulative step length adaptation. IEEE Transactions on Automatic Control, 49(4):617--622, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  5. D. V. Arnold and D. Brauer. On the behaviour of the (1 + 1)-ES for a simple constrained problem. In G. Rudolph et al., editors, Parallel Problem Solving from Nature -- PPSN X, pages 1--10. Springer Verlag, 2008.Google ScholarGoogle Scholar
  6. D. V. Arnold and A. MacLeod. Step length adaptation on ridge functions. Evolutionary Computation, 16(2):151--184, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H.-G. Beyer. Ein Evolutionsverfahren zur mathematischen Modellierung stationärer Zustände in dynamischen Systemen. PhD thesis, Hochschule für Architektur und Bauwesen, Weimar, 1989.Google ScholarGoogle Scholar
  8. H.-G. Beyer and H.-P. Schwefel. Evolution strategies -- A comprehensive introduction. Natural Computing, 1(1):3--52, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. C. A. Coello Coello. Constraint-handling techniques used with evolutionary algorithms. In Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation, pages 2445--2466. ACM Press, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. O. Kramer and H.-P. Schwefel. On three new approaches to handle constraints within evolution strategies. Natural Computing, 5(4):363--385, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. Mallipeddi and P. N. Suganthan. Problem definitions and evaluation criteria for the CEC 2010 Competition on Constrained Real-Parameter Optimization. Technical report, Nanyang Technological University, Singapore, 2010.Google ScholarGoogle Scholar
  12. E. Mezura-Montes and C. A. Coello Coello. A simple multi-membered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 9(1):1--17, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Ostermeier, A. Gawelczyk, and N. Hansen. Step-size adaptation based on non-local use of selection information. In Y. Davidor et al., editors, Parallel Problem Solving from Nature -- PPSN III, pages 189--198. Springer Verlag, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. I. Oyman, K. Deb, and H.-G. Beyer. An alternative constraint handling method for evolution strategies. In Proc. of the 1999 IEEE Congress on Evolutionary Computation, pages 612--619. IEEE Press, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  15. I. Rechenberg. Evolutionsstrategie -- Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Friedrich Frommann Verlag, 1973.Google ScholarGoogle Scholar
  16. T. P. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4(3):274--283, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. P. Runarsson and X. Yao. Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man and Cybernetics -- Part C: Applications and Reviews, 35(2):233--243, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. H.-P. Schwefel. Numerical Optimization of Computer Models. Wiley, 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, 1995.Google ScholarGoogle ScholarDigital LibraryDigital Library

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              cover image ACM Conferences
              GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
              July 2011
              2140 pages
              ISBN:9781450305570
              DOI:10.1145/2001576

              Copyright © 2011 ACM

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              Publication History

              • Published: 12 July 2011

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