Evolutionary algorithms (EAs) have been a popular optimization tool for two decades in academia. However, the industry applications of EAs to real-world optimization problems are infrequent, due to the strong assumption that objective function evaluations are straightforward and easily accessed. In fact, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Such problems driven by only historical data are formulated as offline data-driven optimization problems, which poses challenges to conventional EAs. Although surrogate models that approximate objective functions can be used as function evaluations in EAs to guide the search, the search accuracy is limited by the offline data and cannot be validated or enhanced by sampling new data. The research on offline data-driven evolutionary optimization has not received sufficient attention, although techniques for solving such problems are very practical. One main reason is the lack of benchmark problems that can closely reflect real-world challenges, which leads to a big gap between academia and industries.

In this competition, there are two tracks: constrained and integer single-objective optimization. We carefully select 4 benchmark problems for each track from two real-world applications (aerodynamic optimization and software configuration tuning). The objective functions of those problems cannot be calculated analytically, but can be calculated by calling a simulation to provide true black-box evaluations for offline data sampling and the final comparison. Participants are required to find the optimal solutions of each benchmark problem based on the provided offline data. To assess the performance of compared algorithms, we will evaluate the found optimal solutions using the same simulation which generates the offline data.

**Track 1: constrained single-objective optimization (aerodynamic optimization)**:

**DDCSOP1**: This problem is a drag minimization of an RAE 2822 transonic airfoil subject to lift, pitching moment and sectional area constraints, which has 18 decision variables and 3 constraints (not smaller than 0).

**DDCSOP2**: This problem is a drag minimization of the wing of CRM wing-body configuration subject to pitching-moment coefficient and internal volume of the wing, which has 39 decision variables and 2 constraints (not smaller than 0).

**DDCSOP3**: This problem is a drag minimization of the wing of DLR-F4 wing configuration subject to lift coefficient and thickness of different positionis of the wing, which has 48 decision variables and 5 constraints (not smaller than 0).

**DDCSOP4**: This problem is a drag minimization of the wing of M6 wing configuration subject to lift coefficient and thickness of different positionis of the wing, which has 60 decision variables and 4 constraints (not smaller than 0).

**Track 2: integer single-objective optimization (software configuration tuning)**:

**DDISOP1**: This problem is a maximization of throughput, which has 10 integer decision variables.

**DDISOP2**: This problem is a maximization of requests per second, which has 20 integer decision variables.

**DDISOP3**: This problem is a minimization of latency, which has 30 integer decision variables.

**DDISOP4**: This problem is a minimization of latency, which has 40 integer decision variables.

For both tracks, the boundary of each decision variable is the range between the maximum and minimum of the corresponding dimension in the offline data.

**Download links**: https://github.com/HandingWang/DDEO-WCCI2020

**Important Dates**:

For participants planning to submit a paper to the 2020 IEEE World Congress on Computational Intelligence:

Paper submission: **15 Jan 2020**

Notification to authors: 15 Mar 2020

Final submission: 15 Apr 2020

**Note**: You are encouraged to submit your paper to the Special Session on Data-Driven Optimization of Computationally Expensive Problems

For other participants (**only result entry but without a paper**):
**Results submission deadline: 1 May 2020**

**Note**: Please send your results directly to Dr Handing Wang (hdwang@xidian.edu.cn)

**Organizers:**:

Handing Wang, School of Artificial Intelligence, Xidian University, China

Liang Bao, School of Software, Xidian University, China

Fei Liu, School of Aeronautics, Northwestern Polytechnical University, China

Zhonghua Han, School of Aeronautics, Northwestern Polytechnical University, China

Yaochu Jin, Department of Computer Science, University of Surrey, UK