Abstract
In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for multiobjective optimization is presented. The algorithm performs actual analysis for the initial population and periodically every few generations. An external archive of the unique solutions evaluated using the actual analysis is maintained to train the surrogate models. The data points in the archive are split into multiple partitions using k-Means clustering. A Radial Basis Function (RBF) network surrogate model is built for each partition using a fraction of the points in that partition. The rest of the points in the partition are used as a validation data to decide the prediction accuracy of the surrogate model. Prediction of a new candidate solution is done by the surrogate model with the least prediction error in the neighborhood of that point. Five multiobjective test problems are presented in this study and a comparison with Nondominated Sorting Genetic Algorithm II (NSGA-II) is included to highlight the benefits offered by our approach. EASDS algorithm consistently reported better nondominated solutions for all the test cases for the same number of actual evaluations as compared to a single global surrogate model and NSGA-II.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Radial Basis Function
- Pareto Front
- Multiobjective Optimization
- Radial Basis Function Network
- Nondominated Solution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing - A Fusion of Foundations, Methodologies and Applications 9, 3–12 (2005)
Wilson, B., Cappelleri, D., Simpson, T.W., Frecker, M.: Efficient pareto frontier exploration using surrogate approximations. Optimization and Engineering 2, 31–50 (2001)
Goel, T., Haftka, R.T., Shyy, W., Queipo, N.V.: Ensemble of surrogates. Structural and Multidisciplinary Optimization 33, 199–216 (2007)
Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum Associates, Mahwah, NJ (1985)
Zhou, Z.Z, Ong, Y.S, Nair, P.B, Keane, A.J, Lum, K.Y: Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 37, 66–76 (2007)
Gaspar-Cunha, A., Vieira, A.S.: A hybrid multi-objective evolutionary algorithm using an inverse neural network. In: Hybrid Metaheuristics (HM 2004) Workshop at ECAI 2004, Valencia, Spain (2004)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Abney, S., Schapire, R.E., Singer, Y.: Boosting applied to tagging and PP attachment. In: Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (1999)
Zhao, Y., Gao, J., Yang, X.: A survey of neural network ensembles. In: International Conference on Neural Networks and Brain (ICNN&B 2005), vol. 1, pp. 438–442 (2005)
Jin, Y., Sendhoff, B.: Reducing fitness evaluations using clustering techniques and neural networks ensembles. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 688–699. Springer, Heidelberg (2004)
Hamza, K., Saitou, K.: Vehicle crashworthiness design via a surrogate model ensemble and a coevolutionary genetic algorithm. In: Proceedings of IDETC/CIE 2005 ASME 2005 International Design Engineering Technical Conference, California, USA (September 2005)
Zerpa, L.E., Queipo, N.V., Pintos, S., Salager, J.L.: An optimization methodology of alkaline-surfactant-polymer flooding processes using field scale numerical simulation and multiple surrogates. Journal of Petroleum Science and Engineering 47, 197–208 (2005)
Zhou, Z., Ong, Y.S., Lim, M.H., Lee, B.S.: Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications 11, 957–971 (2007)
Nain, P., Deb, K.: A computationally effective multi-objective search and optimization techniques using coarse-to-fine grain modeling. In: 2002 PPSN Workshop on Evolutionary Multiobjective Optimization (2002)
Ray, T., Smith, W.: Surrogate assisted evolutionary algorithm for multiobjective optimization. In: Proceedings of 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp. 1–8 (2006)
Nain, P.K.S., Deb, K.: A multi-objective optimization procedure with successive approximate models. Technical Report 2005002, KanGAL, IIT Kanpur (2005)
Knowles, J.: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 10, 50–66 (2006)
Emmerich, M.T.M., Giannakoglou, K.C., Naujoks, B.: Single and multiobjective evolutionary optimization assisted by gaussian random field metamodels. IEEE Transactions on Evolutionary Computation 10, 421–439 (2006)
Chafekar, D., Shi, L., Rasheed, K., Xuan, J.: Multiobjective ga optimization using reduced models. Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 35, 261–265 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Deb, K., Agrawal, S.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)
Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26, 30–45 (1996)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31, 265–323 (1999)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the Parallel Problem Solving from Nature VI, pp. 849–858 (2000)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley and Sons, Chichester (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Isaacs, A., Ray, T., Smith, W. (2007). An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-76931-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76930-9
Online ISBN: 978-3-540-76931-6
eBook Packages: Computer ScienceComputer Science (R0)