Rommel G. Regis
ABSTRACT
This paper presents a novel surrogate-assisted evolutionary programming (EP) method for high dimensional constrained black-box optimization with many black-box inequality constraints. A cubic radial basis function (RBF) surrogate is used and the resulting RBF-assisted EP outperforms a standard EP, an RBF-assisted penalty-based EP, Stochastic Ranking Evolution Strategy and Scatter Search on a 124-D automotive problem with 68 black-box constraints.
- J. A. Egea, M. Rodriguez-Fernandez, J. R. Banga and R. Marti. Scatter Search for chemical and bioprocess optimization. Journal of Global Optimization, 37(3):481--503, 2007. Google Scholar
Digital Library
- D. R. Jones. Large-scale multi-disciplinary mass optimization in the auto industry. Presented at the Modeling and Optimization: Theory and Applications (MOPTA) 2008 Conference, Ontario, Canada. 2008.Google Scholar
- M. Kazemi, G. G. Wang, S. Rahnamayan and K. Gupta. Metamodel-based optimization for problems with expensive objective and constraint functions. ASME Journal of Mechanical Design, 133(1): 014505, 2011. doi:10.1115/1.4003035.Google ScholarCross Ref
- J. Kim and H. Myung. Evolutionary programming techniques for constrained optimization problems. IEEE Transactions On Evolutionary Computation, 1(2):129--140, July 1997. Google ScholarDigital Library
- E. Mezura-Montes and C. A. C. Coello. A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 9(1):1--17, 2005. Google ScholarDigital Library
- Y. S. Ong, P. B. Nair and A. J. Keane. Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA JOURNAL, 41(4):687--696, April 2003.Google ScholarCross Ref
- R. G. Regis. Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions. Computers and Operations Research, 38:837--853, 2011. Google ScholarDigital Library
- T. P. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, 2000. Google ScholarDigital Library
- L. Shi and K. Rasheed. ASAGA: an adaptive surrogate-assisted genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008), pages 1049--1056. 2008. Google ScholarDigital Library
Index Terms
- Surrogate-assisted evolutionary programming for high dimensional constrained black-box optimization
Recommendations
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving ...
An efficient evolutionary programming algorithm using mixed mutation operators for numerical optimization
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationEvolutionary algorithms often suffer from premature convergence when dealing with complex multi-modal function optimization problems as the fitness landscape may contain numerous local optima. To avoid premature convergence, sufficient amount of genetic ...
Use of the q-Gaussian mutation in evolutionary algorithms
Special issue on advances in computational intelligence and bioinformaticsThis paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed ...
Comments