Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

By Handing Wang, Yaochu Jin, Chaoli Sun, John James Doherty. 2019

213 citations recorded by the source corpus.

In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such problems are known as offline data-driven optimization problems. Since the surrogate models solely depend on the given historical data, the optimization algorithm is able to search only in a very limited decision space during offline data-driven optimization. This paper proposes a new offline data-driven evolutionary algorithm to make the full use of the offline data to guide the search. To this end, a surrogate management strategy based on ensemble learning techniques developed in machine learning is adopted, which builds a large number of surrogate models before optimization and adaptively selects a small yet diverse subset of them during the optimization to achieve the best local approximation accuracy and reduce the computational complexity. Our experimental results on the benchmark problems and a transonic airfoil design example show that the proposed algorithm is able to handle offline data-driven optimization problems with up to 100 decision variables.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yaochu Jin
2. Mengjie Zhang
3. Bing Xue
4. Handing Wang
5. Yanan Sun
6. Gary G. Yen
7. Tinkle Chugh
8. Kaisa Miettinen

In Computational Theory and Mathematics

1. Yaochu Jin
2. Handing Wang
3. Mengjie Zhang
4. Bing Xue
5. Yanan Sun
6. Gary G. Yen
7. Tinkle Chugh
8. Kaisa Miettinen

Citations in the corpus

• Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “According to [42], SAEAs can be divided into online and offline algorithms, which depend on whether expensive fitness evaluations are used to enrich the training data during the optimization process.”
• A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, methodology): “A wide range of regression models have been adopted as the surrogates, such as Kriging model (Gaussian processes regression model) [13], [14], radial basis function (RBF) networks [15]–[18], polynomial regression [19], and artificial neural networks [20], [21].”
• Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “In addition, ensemble learning [35] can repeatedly use the training data to enhance the search performance in offline data-driven optimization.”