Abstract
Parallel Surrogate-Assisted Evolutionary Algorithms (P-SAEAs) are based on surrogate-informed reproduction operators to propose new candidates to solve computationally expensive optimization problems. Differently, Parallel Surrogate-Driven Algorithms (P-SDAs) rely on the optimization of a surrogate-informed metric of promisingness to acquire new solutions. The former are promoted to deal with moderately computationally expensive problems while the latter are put forward on very costly problems. This paper investigates the design of hybrid strategies combining the acquisition processes of both P-SAEAs and P-SDAs to retain the best of both categories of methods. The objective is to reach robustness with respect to the computational budgets and parallel scalability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Briffoteaux, G., et al.: Parallel surrogate-assisted optimization: batched Bayesian neural network-assisted GA versus q-EGO. Swarm Evol. Comput. 57, 100717 (2020)
Rehback, F., Zaefferer, M., Stork, J., Bartz-Beielstein, T.: Comparison of parallel surrogate-assisted optimization approaches. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, pp. 1348–1355, New York, NY, USA, 2018. Association for Computing Machinery
Wang, H., Jin, Y., Doherty, J.: Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Trans. Cybern. 47(9), 2664–2677 (2017)
Jin, Y., Sendhoff, B.: Reducing fitness evaluations using clustering techniques and neural network ensembles, pp. 688–699 (2004)
Deb, K., Nain, P.: An evolutionary multi-objective adaptive meta-modeling procedure using artificial neural networks. In: Evolutionary Computation in Dynamic and Uncertain Environments, vol. 51, pp. 297–322 (2007). https://doi.org/10.1007/978-3-540-49774-5_13
Regis, R., Shoemaker, C.: A stochastic radial basis function method for the global optimization of expensive functions. INF. J. Comput. 19, 497–509 (2007)
Emmerich, M.T.M., Giannakoglou, K.C., Naujoks, B.: Single- and multiobjective evolutionary optimization assisted by gaussian random field metamodels. IEEE Trans. Evol. Comput. 10(4), 421–439 (2006)
Liu, J., Song, W., Han, Z., Zhang, Y.: Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models. Struct. Multidiscip. Optim. 55, 03 (2017)
Rasmussen, C.E.: Gaussian processes for machine learning. MIT Press (2006)
Gal, Y.: Uncertainty in Deep Learning, Ph. D. thesis, University of Cambridge (2016)
Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evolut. Comput. 1(2), 61 – 70 (2011). https://doi.org/10.1016/j.swevo.2011.05.001
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)
Briffoteaux, G.: Parallel surrogate-based algorithms for solving expensive optimization problems, Ph. D. thesis, Université de Mons, Université de Lille (2022)
Ginsbourger, D., Le Riche, R., Carraro, L.: Kriging is well-suited to parallelize optimization. In: Tenne, Y., Goh, C.-K. (eds.) Computational Intelligence in Expensive Optimization Problems. ALO, vol. 2, pp. 131–162. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-10701-6_6
Briffoteaux, G., Ragonnet, R., Mezmaz, M., Melab, N., Tuyttens, D.: Evolution Control Ensemble Models for Surrogate-Assisted Evolutionary Algorithms. In: High Performance Computing and Simulation 2020, Barcelona, Spain, March 2021
Tian, J., Tan, Y., Zeng, J., Sun, C., Jin, Y.: Multiobjective infill criterion driven gaussian process-assisted particle swarm optimization of high-dimensional expensive problems. IEEE Trans. Evolut. Comput. 23(3), 459–472 (2019)
Claeson, M., Hanson, S.: Covid-19 and the swedish enigma. Lancet 397(10271), 259–261 (2021)
Medicalxpress. Weaker virus? herd immunity? omicron sparks cautious hopes. https://medicalxpress.com/news/2022-01-weaker-virus-herd-immunity-omicron.html (2022)
Michalewicz, Z., Dasgupta, D., Le Riche, R.G., Schoenauer, M.: Evolutionary algorithms for constrained engineering problems. Comput. Ind. Eng. 30(4), 851–870 (1996)
Trauer, J.M.C., Ragonnet, R., Doan, T.N., McBryde, E.S.: Modular programming for tuberculosis control, the “autumn” platform. BMC Infect. Dis. 17(1), 546 (2017)
Caldwell, J.M., et al. Modelling covid-19 in the philippines: technical description of the model. Technical report, Monash University, 2020
Ragonnet, R., et al.: Optimising social mixing strategies achieving COVID-19 herd immunity while minimising mortality in six European countries. medRxiv (2020)
Prem, K., Cook, A.R., Jit, M.: Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLOS Comput. Biol. 13(9), 1–21 (2017)
Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1177–1178, New York, NY, USA, 2010. Association for Computing Machinery
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the symposium on Discrete algorithms, pp. 1027–1035 (2007)
Cappello, F., et al.: Grid’5000: a large scale and highly reconfigurable grid experimental testbed. In: The 6th IEEE/ACM International Workshop on Grid Computing (2005)
Gardner, J.R., Pleiss, G., Bindel, D., Weinberger, K.Q., Wilson, A.G.: GpyTorch: blackbox matrix-matrix gaussian process inference with GPU acceleration. In: Advances in Neural Information Processing Systems (2018)
Chollet, F.: Keras. https://keras.io (2015)
Briffoteaux, G.: pysbo: python framework for surrogate-based optimization. https://pysbo.readthedocs.io/ (2021)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Talbi, E.G.: Metaheuristics: from design to implementation. Wiley, Wiley Series on Parallel and Distributed Computing (2009)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Jin, Y., Olhofer, M., Sendhoff, B.: Managing approximate models in evolutionary aerodynamic design optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 592–599 (2001)
Buche, D., Schraudolph, N.N., Koumoutsakos, P.: Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 35(2), 183–194 (2005)
Acknowledgment
We thank Romain Ragonnet and the Department of Public Health and Preventive Medicine at Monash University in Melbourne, Australia, for helping us to set the AuTuMN simulator.
Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Briffoteaux, G., Melab, N., Mezmaz, M., Tuyttens, D. (2022). Hybrid Acquisition Processes in Surrogate-Based Optimization. Application to Covid-19 Contact Reduction. In: Mernik, M., Eftimov, T., Črepinšek, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2022. Lecture Notes in Computer Science, vol 13627. Springer, Cham. https://doi.org/10.1007/978-3-031-21094-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-21094-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21093-8
Online ISBN: 978-3-031-21094-5
eBook Packages: Computer ScienceComputer Science (R0)