Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin, Yaochu; Wang, Handing; Chugh, Tinkle; Guo, Dan; Miettinen, Kaisa

doi:10.1109/tevc.2018.2869001

Cited by 463 publications

(170 citation statements)

References 108 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…Hence, the main difficulty is to ensure the convergence of the obtained solution set. 1 We do not give the result of DDMOP7 as the number of the obtained non-dominated solutions is too small, and it is meaningless to display the result. DDMOP2, DDMOP3, and DDMOP5 are problems with three objectives.…”

Section: General Shape Of the Approximate Pareto Frontmentioning

confidence: 98%

“…solutions are used to approximate the POFs. 1 Note that we do not give the objective values of the obtained solutions, since we cannot ensure the obtained solutions are exactly on the POFs due to the computationally expensive cost of the real function evaluations. For DDMOP1 in Fig.…”

Section: General Shape Of the Approximate Pareto Frontmentioning

confidence: 99%

“…In fact, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for evaluations. Such problems driven by data collected in simulations or experiments are formulated as data-driven optimization problems [1], which pose challenges to conventional EMO algorithms. Firstly, obtaining the minimum data for conventional EMO algorithms to converge requires a high computational or resource cost [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A repository of real-world datasets for data-driven evolutionary multiobjective optimization

Tian

Wang

et al. 2019

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications.

show abstract

Section: General Shape Of the Approximate Pareto Frontmentioning

confidence: 98%

Section: General Shape Of the Approximate Pareto Frontmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A repository of real-world datasets for data-driven evolutionary multiobjective optimization

Tian

Wang

et al. 2019

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, each evaluation of the objective functions in evolutionary optimization of the neural networks requires the training of the model, which can be computationally intensive if the amount of data is large. To address this issue, surrogateassisted evolutionary optimization [29], [30] or Bayesian optimization [31] are helpful to reduce the computation cost.…”

Section: )mentioning

confidence: 99%

Multi-Objective Evolutionary Federated Learning

Zhu

Jin

2020

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

227

View full text Add to dashboard Cite

Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to optimize the structure of the neural network models in federated learning using a multiobjective evolutionary algorithm to simultaneously minimize the communication costs and the global model test errors. A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks. Experimental results on both multilayer perceptrons and convolutional neural networks indicate that the proposed optimization method is able to find optimized neural network models that can not only significantly reduce communication costs but also improve the learning performance of federated learning compared with the standard fully connected neural networks.

show abstract

“…Off-line data-driven optimization problems widely exist in the real world [11], [36], such as trauma systems design [6], performance optimization of fused magnesium furnaces [7], and operational indices optimization of beneficiation processes [8], in which the objective functions cannot be directly calculated using mathematical equations and only data are available for fitness evaluations [11], [36]. Off-line data-driven optimization starts with a certain amount of collected data, which are used to construct surrogates for searching optimal solutions [9].…”

Section: Related Work a Off-line Data-driven Optimization Problemsmentioning

confidence: 99%

Off-line Data-driven Multi-objective Optimization: Knowledge Transfer between Surrogates and Generation of Final Solutions

Yang

Ding

Jin

et al. 2020

IEEE Trans. Evol. Computat.

Self Cite

View full text Add to dashboard Cite

In off-line data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate aims to guide the algorithm to quickly find a promising sub-region in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the coarse surrogate. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art off-line datadriven multi-objective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes.Index Terms-Off-line data-driven optimization; multisurrogate; knowledge transfer; multi-objective evolutionary algorithms.

show abstract

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Cited by 463 publications

References 108 publications

A repository of real-world datasets for data-driven evolutionary multiobjective optimization

A repository of real-world datasets for data-driven evolutionary multiobjective optimization

Multi-Objective Evolutionary Federated Learning

Off-line Data-driven Multi-objective Optimization: Knowledge Transfer between Surrogates and Generation of Final Solutions

Contact Info

Product

Resources

About