Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

By Yew-Soon Ong, Prasanth B. Nair, Andy J. Keane. 2003

607 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e2df5722efba30e95fdea26f5ea546986d19b165

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Citations in the corpus

Memetic Computation—Past, Present & Future [Research Frontier] (2010, background): “[20] [26] What form of lifetime learning or 6) local learning methods or otherwise referred to as meme(s) to use?”
Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation (2012, background): “Ong et al., 2003 ) or support vector regression (e.g.”
Response surface approximation of pareto optimal front in multi-objective optimization (2004, methodology): “[32] used radial basis functions to approximate the objective function and constraints and then used a combination of evolutionary algorithm and sequential quadratic programming to find optimal solutions of an aircraft wing design problem with single objective.”
Surrogate-assisted evolutionary computation: Recent advances and future challenges (2011, background): “One intensively researched area is surrogate-assisted design optimization, such as turbine blades [9,23,84,85], airfoils [27,86], forging [87], vehicle crash tests [88], multi-processor systems-on-chip design [89] and injection systems [90].”
A comprehensive survey of fitness approximation in evolutionary computation (2005, background): “In [56], local approximate models are constructed only for local search within a Lamarkian framework of evolution.”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, neutral): “Detailed reviews of all these meta-model based EAs can now be found in recent review papers [48], [34], [33], along with new approaches that try to manage the metamodels optimally with respect to the conicting concerns of optimization and the design of experiments (i.e. improving the model) [ 47 ], ”
Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “In [167, 168], memetic frameworks combining an EA as a global search, where at each generation every non-duplicated vector in the population is refined using a TR, has been proposed.”
Neuroevolutionary reinforcement learning for generalized control of simulated helicopters (2011, background): “However, it is most commonly used to reduce computation costs [37, 59, 67, 68, 70] by finding a model that is faster than the original fitness function.”
Policy Evaluation Networks (2020, background): “…is the analogue of our PVNs, also called ‘surrogate models’ or ‘response sur-faces’ which aim to generalize across the ﬁtness landscape, for example (Booker et al., 1998; Box & Wilson, 1951; Moore & Schneider, 1996; Ong et al., 2003; Loshchilov et al., 2012); see (Jones, 2001) for an overview.”
Evolutionary optimization in uncertain environments-a survey (2005, background): “One good example is structural design optimization [84], [110], [120], [145], [149], [156], [188].”
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling (2007, background): “In addition, a study in [ 30 ] focused on the surrogate-assisted MAs framework for computationally expensive problems and robust designing problems.”
Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, methodology): “The most commonly used surrogate models include polynomial regression [19], also known as response surface methodology [19], support vector machines [20]–[22], ANNs [12], [23], [24], radial basis function (RBF) networks [25]–[29], and Gaussian processes (GPs), also referred as to Kriging or design a”
Surrogate-Assisted Learning of Neural Networks (2017, background): “• Our hypothesis is that assisting this learning process by means of surrogate-assisted optimization, which has proven to be able to perform signiﬁcantly well in expensive industrial optimization tasks [14, 15], should be beneﬁcial.”
A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “built for constraints [29], [45], [46].”
A Multi-Facet Survey on Memetic Computation (2011, background): “dynamic design [30], atomic and molecular structure problems [37], optimal control systems with machine learning [38]–[43], and computationally expensive and uncertain environments [44], [45].”