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Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging …
Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, Kalyanmoy Deb
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Reference Vector-Assisted Adaptive Model Management for Surrogate-Assisted Many-Objective Optimization
Acquisition functions for surrogate-assisted many-objective optimization require a delicate balance between convergence and diversity. …
Qiqi Liu, Ran Cheng, Yaochu Jin, Martin Heiderich, Tobias Rodemann
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A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A
Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space …
Danial Yazdani, Ran Cheng, Donya Yazdani, Jürgen Branke, Yaochu Jin, Xin Yao
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RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
Despite the remarkable successes of convolutional neural networks (CNNs) in computer vision, it is time-consuming and error-prone to …
Hao Tan, Ran Cheng, Shihua Huang, Cheng He, Changxiao Qiu, Fan Yang, Ping Luo
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Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems
Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex …
Chaoli Sun, Yaochu Jin, Ran Cheng, Jinliang Ding, Jianchao Zeng
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A Multi-swarm Evolutionary Framework based on a Feedback Mechanism
Most evolutionary algorithms, including particle swarm optimization (PSO) algorithms, involve at least one population (swarm) to …
Ran Cheng, Chaoli Sun, Yaochu Jin
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