Fitness Approximation in Evolutionary Computation

Bibliography

Fitness evaluations in evolutionary computation are not trivial in many real-world applications. In the following cases, a computationally efficient approximation of the original fitness function is necessary:
  • The fitness function is computationally very expensive,
  • No explicit mathematical fitness function is available,
  • The original fitness function is noisy or multi-modal.


  • Approximate models are also known as meta-models or surrogates in optimization. Polynomials (response surface methodologies), neural networks, such as multi-layer perceptrons (MLPs), radial-basis-function (RBF) networks, support vector machines (SVMs), Gaussian processes, and Kriging models (often polynomials plus Gaussian processes) can be used for constructing meta-models.

    In evolutionary optimization, particular methods, such as fitness inheritance, fitness imitation and fitness assignment can also be employed.

    References on handling noisy fitness functions and search for robust optimal solutions can be found here.

    A survey of the research can be found here.

    Please send me an email if you hope to include your work in this bibliography.

    Last updated on January, 2011.



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      1. Salem Fawaz Adra, Ian Griffin, and Peter J. Fleming. An informed convergence accelerator for evolutionary multiobjective optimiser. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, New York, NY, USA, pages 734--740, 2007. ACM. [bibtex-key = Adra07] [bibtex-entry]


      2. J.S. Aguilar-Ruiz, D. Mateos, and D.S. Rodriguez. Evolutionary Neuroestimation of Fitness Functions. In Lecture Notes on Artificial Inteligence, volume 2902, pages 74--83, 2003. [bibtex-key = Aguilar-Ruiz03] [bibtex-entry]


      3. M.R. Akbarzadeh-T, M. Davarynejad, and N. Pariz. Adaptive fuzzy fitness granulation for evolutionary optimization.. International Journal of Approximate Reasoning, 49(3):523--538, 2008. [bibtex-key = Davarynejad08b] [bibtex-entry]


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      5. K. Anderson and Y. Hsu. Genetic crossover strategy using an approximation concept. In IEEE Congress on Evolutionary Computation, Washington D.C., pages 527-533, 1999. IEEE. [bibtex-key = Anderson99] [bibtex-entry]


      6. Manuel Barros, Jorge Guilherme, and Nuno Horta. GA-SVM feasibility model and optimization kernel applied to analog IC design automation. In GLSVLSI '07: Proceedings of the 17th ACM Great Lakes symposium on VLSI, New York, NY, USA, pages 469--472, 2007. ACM. [bibtex-key = Barros07] [bibtex-entry]


      7. Ricardo Landa Becerra, Luis V. Santana-Quintero, and Carlos A. Coello Coello. Knowledge Incorporation in Multi-objective Evolutionary Algorithms. In Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, pages 23--46. 2008. [bibtex-key = Becerra08] [bibtex-entry]


      8. Maumita Bhattacharya. Reduced computation for evolutionary optimization in noisy environment. In GECCO '08: Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation, New York, NY, USA, pages 2117--2122, 2008. ACM. [bibtex-key = 1389033] [bibtex-entry]


      9. M. Bhattacharya and G. Lu. A dynamic approximate fitness based hybrid EA for optimization problems. In Proceedings of IEEE Congress on Evolutionary Computation, pages 1879--1886, 2003. [bibtex-key = Bhatta03] [bibtex-entry]


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      12. J. Branke and C. Schmidt. Fast convergence by means of fitness estimation. Soft Computing Journal, 9(1):13-20, 2005. [bibtex-key = Branke05] [bibtex-entry]


      13. J. Branke, C. Schmidt, and H. Schmeck. Efficient fitness estimation in noisy environment. In L. Spector et al, editor, Proceedings of Genetic and Evolutionary Computation, San Francisco, CA, pages 243-250, July 2001. Morgan Kaufmann. [bibtex-key = Branke01] [bibtex-entry]


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      17. L. Bull. On model-based evolutionary computation. Soft Computing, 3:76-82, 1999. [bibtex-key = Bull99] [bibtex-entry]


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      20. D. Chafekar, L. Shi, K. Rasheed, and J. Xuan. Multi-objective GA optimization using reduced models. IEEE Trans. on Systems, Man, and Cybernetics: Part C, 9(2):261--265, 2005. [bibtex-key = Chafekar05] [bibtex-entry]


      21. J.-H. Chen, D.E. Goldberg, S.-Y. Ho, and K. Sastry. Fitness inheritance in multi-objective optimization. In Proceedings of genetic and Evolutionary Computation Conference, pages 319-326, 2002. Morgan Kaufmann. [bibtex-key = Chen02] [bibtex-entry]


      22. Haixia Chen, Senmiao Yuan, and Kai Jiang. Fitness Approximation in Estimation of Distribution Algorithms for Feature Selection.. In Shichao Zhang and Ray Jarvis, editors, Australian Conference on Artificial Intelligence, volume 3809 of Lecture Notes in Computer Science, pages 904--909, 2005. Springer. [bibtex-key = ChenYJ05] [bibtex-entry]


      23. H.-S. Chung and J. J. Alonso. Multi-objective optimization using approximation model-based genetic algorithms. Technical report 2004--4325, AIAA, 2004. [bibtex-key = ChAl04] [bibtex-entry]


      24. M. Davarynejad, M.R. Akbarzadeh-T, and Carlos A. Coello Coello. Auto-tuning fuzzy granulation for evolutionary optimization. In CEC 2008, IEEE World Congress on Evolutionary Computation, pages 3572--3579, June 2008. [bibtex-key = Davarynejad08a] [bibtex-entry]


      25. M. Davarynejad, M.R. Akbarzadeh-T, and N. Pariz. A novel general framework for evolutionary optimization: Adaptive fuzzy fitness granulation.. In IEEE Congress on Evolutionary Computation, pages 951--956, 2007. IEEE. [bibtex-key = Davarynejad07] [bibtex-entry]


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      27. E. Ducheyne, B. De Baets, and R. de Wulf. Is fitness inheritance useful for real-world applications?. In Second International Conference on Multi-criterion Optimization, LNCS 2632, pages 31--42, 2003. Springer. [bibtex-key = Ducheyne03] [bibtex-entry]


      28. D. Eby, R. Averill, W. Punch, and E. Goodman. Evaluation of injection island model GA performance on flywheel design optimization. In Third Conference on Adaptive Computing in Design and manufacturing, pages 121-136, 1998. Springer. [bibtex-key = Eby98] [bibtex-entry]


      29. M.A. El-Beltagy and A.J. Keane. Evolutionary optimization for computationally expensive problems using Gaussian processes. In Proceedings of International Conference on Artificial Intelligence, pages 708--714, 2001. CSREA. [bibtex-key = Beltagy01] [bibtex-entry]


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      31. M.A. El-Beltagy, P.B. Nair, and A.J. Keane. Metamodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations. In Proceedings of Genetic and Evolutionary Conference, Orlando, pages 196-203, 1999. Morgan Kaufmann. [bibtex-key = Beltagy99] [bibtex-entry]


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      35. M. Farina. A neural network based generalized response surface multiobjective evolutionary algorithms. In Congress on Evolutionary Computation, pages 956-961, 2002. IEEE Press. [bibtex-key = Farina02] [bibtex-entry]


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      37. M. Farina and P. Amato. Linked interpolation-optimization strategies for multicriteria optimization problems. Soft Computing, 9(1):54-65, 2005. [bibtex-key = Farina05] [bibtex-entry]


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      40. Aaron Garrett, Gerry Dozier, and Kalyanmoy Deb. NEMO: neural enhancement for multiobjective optimization. In IEEE Congress on Evolutionary Computation, pages 3108--3113, 2007. [bibtex-key = GarrettDD07] [bibtex-entry]


      41. C.A. Georgopoulou and K.C. Giannakoglou. Multiobjective Metamodel-Assisted Memetic Algorithms. In Multi-objective Memetic Algorithms By Chi-Keong Goh, pages 153--181, 2009. Springer Berlin / Heidelberg. [bibtex-key = Geor09] [bibtex-entry]


      42. A. Giotis, M. Emmerich, B. Naujoks, and K. Giannakoglou. Low kost stochastic optimization for engineering applications. In Proceedings of International Conference on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, 2001. [bibtex-key = Giotis01] [bibtex-entry]


      43. A.A. Giunta and L. Watson. A comparison of approximation modeling techniques: Polynomial versus interpolating models. Technical report 98-4758, AIAA, 1998. [bibtex-key = Giunta98] [bibtex-entry]


      44. T. Goel, R. Haftka, W. Shyy, N. Queipo, R. Vaidyanathan, and K. Tucker. Response Surface Approximation of Pareto Optimal front in Multi-Objective Optimization. Computer Methods in Applied Mechanics and Engineering, 196(4-6):879--893, 1 January 2007. [bibtex-key = Queipo07] [bibtex-entry]


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      49. Shin-Ming Guo. A Fast Multi-Objective Evolutionary Algorithm for Expensive Simulation Optimization Problems. In ICICIC '07: Proceedings of the Second International Conference on Innovative Computing, Informatio and Control, Washington, DC, USA, pages 324, 2007. IEEE Computer Society. [bibtex-key = Guo07] [bibtex-entry]


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      51. G. I. Hawe and J. K. Sykulski. A Scalarizing One-Stage Algorithm for Efficient Multi-Objective Optimization. IEEE Transactions on Magnetics, 44(6):1094--1097, June 2008. [bibtex-key = Hawe08b] [bibtex-entry]


      52. G. Hawe and J.K. Sykulski. Scalarizing cost-effective multi-objective optimization algorithms made possible with kriging. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 27(4):836--844, July 2008. [bibtex-key = Hawe08a] [bibtex-entry]


      53. D. Hidovic and J.E. Rowe. Validating a model of colon colouration using an evolution strategy with adaptive approximations. In Genetic and Evolutionary Computation Conference, pages 1005--1017, 2004. [bibtex-key = Hidovic04] [bibtex-entry]


      54. Y.-S. Hong, H.Lee, and M.-J. Tahk. Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks. Engineering Optimization, 35(1):91-102, 2003. [bibtex-key = Hong03] [bibtex-entry]


      55. M. Hüscken, Y. Jin, and B. Sendhoff. Structure optimization of neural networks for aerodynamic optimization. Soft Computing Journal, 9(1):21--28, 2005. [bibtex-key = Huesken05] [bibtex-entry]


      56. M. Hüsken, Y. Jin, and B. Sendhoff. Structure optimization of neural networks for evolutionary design optimization. In 2002 GECCO Workshop on Approximation and Learning in Evolutionary Computation, pages 13-16, 2002. [bibtex-key = Huesken02] [bibtex-entry]


      57. M. Hüsken and B. Sendhoff. Evolutionary optimization for problem classes with Lamarckian inheritance. In IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, pages 98-109, 2000. [bibtex-key = Huesken00] [bibtex-entry]


      58. X. Jiang, D. Chafekar, and K. Rasheed. Constrained multi-objective GA optimization using reduced models. In Proceedings of GECCO Workshop on Learning, Adaptation and Approximation in Evolutionary Computation, pages 174--177, 2003. [bibtex-key = Jiang03] [bibtex-entry]


      59. Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing Journal, 9(1):3--12, 2005. [bibtex-key = Jin05b] [bibtex-entry]


      60. Y. Jin and J. Branke. Evolutionary optimization in uncertain environments: A survey. IEEE Transactions on Evolutionary Computation, 9(3):303--317, 2005. [bibtex-key = Jin05a] [bibtex-entry]


      61. R. Jin, W. Chen, and T.W. Simpson. Comparative studies of metamodeling techniques under miltiple modeling criteria. Technical report 2000-4801, AIAA, 2000. [bibtex-key = JinR00] [bibtex-entry]


      62. Y. Jin, M. Huesken, and B. Sendhoff. Quality measures for approximate models in evolutionary computation. In Proceedings of GECCO Workshops: Workshop on Adaptation, Learning and Approximation in Evolutionary Computation, Chicago, pages 170--174, 2003. [bibtex-key = Jin03c] [bibtex-entry]


      63. Y. Jin, M. Hüsken, M. Olhofer, and B. Sendhoff. Neural networks for fitness approximation in evolutionary optimization. In Y. Jin, editor,Knowledge Incorporation in Evolutionary Computation, pages 281--305. Springer, Berlin, 2004. [bibtex-key = Jin04b] [bibtex-entry]


      64. Y. Jin, M. Olhofer, and B. Sendhoff. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5):481-494, 2002. [bibtex-key = Jin02a] [bibtex-entry]


      65. Y. Jin, M. Olhofer, and B. Sendhoff. Managing approximate models in evolutionary aerodynamic design optimization. In Proceedings of IEEE Congress on Evolutionary Computation, volume 1, pages 592-599, May 2001. [bibtex-key = Jin01] [bibtex-entry]


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      67. Y. Jin and B. Sendhoff. Reducing fitness evaluations using clustering techniques and neural networks ensembles. In Genetic and Evolutionary Computation Conference, volume 3102 of LNCS, pages 688--699, 2004. Springer. [bibtex-key = Jin04] [bibtex-entry]


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      82. Lihui Ma, Kunlun Xin, and Suiqing Liu. Using Radial Basis Function Neural Networks to Calibrate Water Quality Model. In Proceedings of World Academy of Science, Engineering and Technology, pages 385--393, 2008. [bibtex-key = Ma08] [bibtex-entry]


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