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An Elitist Genetic Algorithm for Multiobjective Optimization

  • Lino Costa
  • Pedro Oliveira
Chapter
Part of the Applied Optimization book series (APOP, volume 86)

Abstract

Solving multiobjective engineering problems is, in general, a difficult task. In spite of the success of many approaches, elitism has emerged has an effective way of improving the performance of algorithms. In this paper, a new elitist scheme, by which it is possible to control the size of the elite population, as well as the concentration of points in the approximation to the Pareto-optimal set, is introduced. This new scheme is tested on several multiobjective problems and, it proves to lead to a good compromise between computational time and size of the elite population.

Keywords

Genetic algorithms Multiobjective optimization Elitism. 

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Bibliography

  1. K. Deb and D. Goldberg. An investigation of niche and species formation in genetic function optimization. In Proceedings of the Third International Conference on Genetic Algorithms, pages 42–50, USA, 1989.Google Scholar
  2. C.M. Fonseca and P.J. Fleming. On the performance assessment and comparison of stochastic multiobjective optimizers. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature IV, pages 584–593. Springer, 1995.Google Scholar
  3. C.M. Fonseca and P.J. Fleming. Multiobjective optimization and multiple constraint handling with evolutionary algorithms-part ii: Application example. IEEE Transactions on Systems, Man, and Cybernetics: Part A: Systems and Humans, 28 (1): 38–47, 1998.CrossRefGoogle Scholar
  4. D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Massachusetts, 1989.zbMATHGoogle Scholar
  5. J. Horn, N. Nafploitis, and D. Goldberg. A niched pareto genetic algorithm for multi-objective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 82–87, 1994.Google Scholar
  6. J.D. Knowles and D.W. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation,8(2):149172, 2000.CrossRefGoogle Scholar
  7. F. Kursawe. A variant of evolution strategies for vector optimization. In H.-P. Schwefel and R. Manner, editors, Parallel Problem Solving from Nature, pages 193–197. Springer, 1990.Google Scholar
  8. C. Poloni. Genetic Algorithms in engineering and computer science, chapter Hybrid GA for multiobjective aerodynamic shape optimization, pages 397–414. G. Winter, J. Periaux, M. Galan, and P. Puesta, Ed. Hillsdale, 1997.Google Scholar
  9. J.D. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In J.J. Grefensttete, editor, Proceedings of the First International Conference on Genetic Algorithms, pages 93–100. Ed. Hillsdale, 1985.Google Scholar
  10. N. Srinivas and K. Deb. Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation,2(3):221248,1994.CrossRefGoogle Scholar
  11. D.A. Van Veldhuizen and G.B. Lamont. Multiobjective evolutionary algorithms: Analysing the state-of-the-art. Evolutionary Computation,8(2):125–147, 2000.CrossRefGoogle Scholar
  12. E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8: 173–195, 2000.CrossRefGoogle Scholar
  13. E. Zitzler and L. Thiele. Multiobjective optimization using evolutionary algorithms a comparative case study. In Parallel Problem Solving from Nature V, pages 292–301, 1998.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Lino Costa
    • 1
  • Pedro Oliveira
    • 1
  1. 1.Departamento de Produção e SistemasUniversidade do MinhoBragaPortugal

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