Towards Better Integration of Surrogate Models and Optimizers
Chugh, T., Rahat, A., Volz, V., & Zaefferer, M. (2020). Towards Better Integration of Surrogate Models and Optimizers. In T. Bartz-Beielstein, B. Filipič, P. Korošec, & E.-G. Talbi (Eds.), High-Performance Simulation-Based Optimization (pp. 137-163). Springer. Studies in Computational Intelligence, 833. https://doi.org/10.1007/978-3-030-18764-4_7
Published inStudies in Computational Intelligence
© Springer Nature Switzerland AG 2020.
Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be very effective in solving (synthetic and real-world) computationally expensive optimization problems with a limited number of function evaluations. The two main components of SAEAs are: the surrogate model and the evolutionary optimizer, both of which use parameters to control their respective behavior. These parameters are likely to interact closely, and hence the exploitation of any such relationships may lead to the design of an enhanced SAEA. In this chapter, as a first step, we focus on Kriging and the Efficient Global Optimization (EGO) framework. We discuss potentially profitable ways of a better integration of model and optimizer. Furthermore, we investigate in depth how different parameters of the model and the optimizer impact optimization results. In particular, we determine whether there are any interactions between these parameters, and how the problem characteristics impact optimization results. In the experimental study, we use the popular Black-Box Optimization Benchmarking (BBOB) testbed. Interestingly, the analysis finds no evidence for significant interactions between model and optimizer parameters, but independently their performance has a significant interaction with the objective function. Based on our results, we make recommendations on how best to configure EGO. ...
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Chugh, Tinkle; Sun, Chaoli; Wang, Handing; Jin, Yaochu (Springer, 2020)This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is ...
Greiner, David; Periaux, Jacques; Quagliarella, Domenico; Magalhaes-Mendes, Jorge; Galván, Blas (Hindawi Publishing Corporation, 2018)
Chugh, Tinkle (University of Jyväskylä, 2017)Multiobjective optimization problems (MOPs) with a large number of conﬂicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations ...
A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization Habib, Ahsanul; Singh, Hemant Kumar; Chugh, Tinkle; Ray, Tapabrata; Miettinen, Kaisa (Institute of Electrical and Electronics Engineers, 2019)Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years to solve ...
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